sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_3.5.1  magrittr_1.5    tools_3.5.1     htmltools_0.3.6
##  [5] yaml_2.2.0      Rcpp_1.0.0      stringi_1.2.4   rmarkdown_1.11 
##  [9] knitr_1.20      stringr_1.3.1   digest_0.6.18   evaluate_0.12

User Inputs

output.var = params$output.var 
transform.abs = params$transform.abs
log.pred = params$log.pred
eda = params$eda
algo.forward = params$algo.forward
algo.backward = params$algo.backward
algo.stepwise = params$algo.stepwise
algo.LASSO = params$algo.LASSO
algo.LARS = params$algo.LARS
  
algo.forward.caret = params$algo.forward.caret
algo.backward.caret = params$algo.backward.caret
algo.stepwise.caret = params$algo.stepwise.caret
algo.LASSO.caret = params$algo.LASSO.caret
algo.LARS.caret = params$algo.LARS.caret

message("Parameters used for training/prediction: ")
## Parameters used for training/prediction:
str(params)
## List of 14
##  $ output.var         : chr "y3"
##  $ transform.abs      : logi FALSE
##  $ log.pred           : logi FALSE
##  $ eda                : logi TRUE
##  $ algo.forward       : logi FALSE
##  $ algo.backward      : logi FALSE
##  $ algo.stepwise      : logi FALSE
##  $ algo.LASSO         : logi FALSE
##  $ algo.LARS          : logi FALSE
##  $ algo.forward.caret : logi FALSE
##  $ algo.backward.caret: logi FALSE
##  $ algo.stepwise.caret: logi FALSE
##  $ algo.LASSO.caret   : logi FALSE
##  $ algo.LARS.caret    : logi FALSE
# Setup Labels
# alt.scale.label.name = Alternate Scale variable name
#   - if predicting on log, then alt.scale is normal scale
#   - if predicting on normal scale, then alt.scale is log scale
if (log.pred == TRUE){
  label.names = paste('log.',output.var,sep="")
  alt.scale.label.name = output.var
}
if (log.pred == FALSE){
  label.names = output.var
  alt.scale.label.name = paste('log.',output.var,sep="")
}

Prepare Data

Read and Clean Features

features = read.csv("../../Data/features.csv")
features.highprec = read.csv("../../Data/features_highprec.csv")
all.equal(features, features.highprec)
##  [1] "Component \"x11\": Mean relative difference: 0.001401482"     
##  [2] "Component \"stat9\": Mean relative difference: 0.0002946299"  
##  [3] "Component \"stat12\": Mean relative difference: 0.0005151515" 
##  [4] "Component \"stat13\": Mean relative difference: 0.001354369"  
##  [5] "Component \"stat18\": Mean relative difference: 0.0005141104" 
##  [6] "Component \"stat22\": Mean relative difference: 0.001135977"  
##  [7] "Component \"stat25\": Mean relative difference: 0.0001884615" 
##  [8] "Component \"stat29\": Mean relative difference: 0.001083691"  
##  [9] "Component \"stat36\": Mean relative difference: 0.00021513"   
## [10] "Component \"stat37\": Mean relative difference: 0.0004578125" 
## [11] "Component \"stat43\": Mean relative difference: 0.0003473684" 
## [12] "Component \"stat45\": Mean relative difference: 0.0002951699" 
## [13] "Component \"stat46\": Mean relative difference: 0.0009745763" 
## [14] "Component \"stat47\": Mean relative difference: 8.829902e-05" 
## [15] "Component \"stat55\": Mean relative difference: 0.001438066"  
## [16] "Component \"stat57\": Mean relative difference: 0.0001056911" 
## [17] "Component \"stat58\": Mean relative difference: 0.0004882261" 
## [18] "Component \"stat60\": Mean relative difference: 0.0002408377" 
## [19] "Component \"stat62\": Mean relative difference: 0.0004885106" 
## [20] "Component \"stat66\": Mean relative difference: 1.73913e-06"  
## [21] "Component \"stat67\": Mean relative difference: 0.0006265823" 
## [22] "Component \"stat73\": Mean relative difference: 0.003846154"  
## [23] "Component \"stat75\": Mean relative difference: 0.002334906"  
## [24] "Component \"stat83\": Mean relative difference: 0.0005628415" 
## [25] "Component \"stat86\": Mean relative difference: 0.0006104418" 
## [26] "Component \"stat94\": Mean relative difference: 0.001005115"  
## [27] "Component \"stat97\": Mean relative difference: 0.0003551913" 
## [28] "Component \"stat98\": Mean relative difference: 0.0006157635" 
## [29] "Component \"stat106\": Mean relative difference: 0.0008267717"
## [30] "Component \"stat109\": Mean relative difference: 0.0005121359"
## [31] "Component \"stat110\": Mean relative difference: 0.0007615527"
## [32] "Component \"stat111\": Mean relative difference: 0.001336134" 
## [33] "Component \"stat114\": Mean relative difference: 7.680492e-05"
## [34] "Component \"stat117\": Mean relative difference: 0.0002421784"
## [35] "Component \"stat122\": Mean relative difference: 0.0006521084"
## [36] "Component \"stat123\": Mean relative difference: 8.333333e-05"
## [37] "Component \"stat125\": Mean relative difference: 0.002385135" 
## [38] "Component \"stat130\": Mean relative difference: 0.001874016" 
## [39] "Component \"stat132\": Mean relative difference: 0.0003193182"
## [40] "Component \"stat135\": Mean relative difference: 0.0001622517"
## [41] "Component \"stat136\": Mean relative difference: 7.722008e-05"
## [42] "Component \"stat138\": Mean relative difference: 0.0009739953"
## [43] "Component \"stat143\": Mean relative difference: 0.0004845361"
## [44] "Component \"stat146\": Mean relative difference: 0.0005821596"
## [45] "Component \"stat148\": Mean relative difference: 0.0005366922"
## [46] "Component \"stat153\": Mean relative difference: 0.0001557522"
## [47] "Component \"stat154\": Mean relative difference: 0.001351916" 
## [48] "Component \"stat157\": Mean relative difference: 0.0005427928"
## [49] "Component \"stat162\": Mean relative difference: 0.002622951" 
## [50] "Component \"stat167\": Mean relative difference: 0.0005905172"
## [51] "Component \"stat168\": Mean relative difference: 0.0002791096"
## [52] "Component \"stat169\": Mean relative difference: 0.0004121827"
## [53] "Component \"stat170\": Mean relative difference: 0.0004705882"
## [54] "Component \"stat174\": Mean relative difference: 0.0003822894"
## [55] "Component \"stat179\": Mean relative difference: 0.0008286604"
## [56] "Component \"stat184\": Mean relative difference: 0.0007526718"
## [57] "Component \"stat187\": Mean relative difference: 0.0005122768"
## [58] "Component \"stat193\": Mean relative difference: 4.215116e-05"
## [59] "Component \"stat199\": Mean relative difference: 0.002155844" 
## [60] "Component \"stat203\": Mean relative difference: 0.0003738318"
## [61] "Component \"stat213\": Mean relative difference: 0.000667676" 
## [62] "Component \"stat215\": Mean relative difference: 0.0003997955"
head(features)
##     JobName        x1       x2       x3        x4       x5        x6
## 1 Job_00001 2.0734508 4.917267 19.96188  3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
##         x7        x8       x9       x10      x11      x12       x13
## 1 2.979479  8.537228 1.103368 4.6089458 1.05e-07 7.995825 13.215498
## 2 2.388119  6.561461 0.588572 1.0283282 1.03e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.06e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.47e-08 9.548698 17.170635
## 5 2.891535  9.362389 1.246039 1.7333300 1.01e-07 9.596095  5.794567
## 6 1.247838  7.033354 1.852231 0.4839371 1.07e-07 3.810983 23.863169
##        x14       x15       x16      x17      x18      x19      x20
## 1 4.377983 0.2370623  6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121  6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445  8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830  7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382  7.062051 1.341864 7.748325 5.009365 1.725179
##        x21      x22      x23      stat1      stat2      stat3      stat4
## 1 42.36548 1.356213 2.699796  2.3801832  0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480  1.8140973  1.6204884  2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289  1.1415752  2.9805934
## 4 32.60415 1.355396 3.402398  0.4371202 -1.9355906  0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011  2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084  1.8853619  2.4154840 -2.6022179
##          stat5      stat6      stat7      stat8      stat9      stat10
## 1  0.148893163 -0.6622978 -2.4851868  0.3647782  2.5364335  2.92067981
## 2  1.920768980  1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3  2.422584300 -0.4166040  2.2205689 -2.6741531  0.4844292  2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467  1.7960306  0.74771154
## 5 -2.311132430 -1.0166832  2.7269876  1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915  1.4875095  2.0188572 -1.4892503 -1.41103566
##       stat11    stat12     stat13     stat14     stat15     stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812  0.6029300
## 2 -0.8547903  1.119316  0.7227427  0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580  2.865401 -2.9756081  2.9871745  1.9539525 -1.8857163
## 4  1.3982378  1.856765 -1.0379983  2.3341896  2.3057184 -2.8947697
## 5  0.9567220  2.567549  0.3184886  1.0307668  0.1644241 -0.6613821
## 6  0.5341771 -1.461822  0.4402476 -1.9282095 -0.3680157  1.8188807
##        stat17     stat18     stat19     stat20     stat21     stat22
## 1 -1.04516208  2.3544915  2.4049001  0.2633883 -0.9788178  1.7868229
## 2  0.09513074  0.4727738  1.8899702  2.7892542 -1.3919091 -1.7198164
## 3  0.40285346  1.4655282 -1.4952788  2.9162340 -2.3893208  2.8161423
## 4  2.97446084  2.3896182  2.3083484 -1.1894441 -2.1982553  1.3666242
## 5 -0.98465055  0.6900643  1.5894209 -2.1204538  1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904  1.1764175 -2.9100556 -2.1359294
##       stat23     stat24     stat25    stat26     stat27      stat28
## 1 -2.3718851  2.8580718 -0.4719713 -2.817086 -0.9518474  2.88892484
## 2 -2.3293245  1.5577759 -1.9569720  1.554194 -0.5081459 -1.58715141
## 3 -2.5402296  0.1422861  0.3572798 -1.051886 -2.1541717  0.03074004
## 4 -1.9679050 -1.4077642  2.5097435  1.683121 -0.2549745 -2.90384054
## 5  2.0523429 -2.2084844 -1.9280857 -2.116736  1.8180779 -1.42167580
## 6  0.2184991 -0.7599817  2.6880329 -2.903350 -1.0733233 -2.92416644
##       stat29     stat30     stat31     stat32      stat33     stat34
## 1  0.7991088 -2.0059092 -0.2461502  0.6482101 -2.87462163 -0.3601543
## 2  1.9758110 -0.3874187  1.3566630  2.6493473  2.28463054  1.8591728
## 3 -0.4460218  1.0279679  1.3998452 -1.0183365  1.41109037 -2.4183984
## 4  1.0571996  2.5588036 -2.9830337 -1.1299983  0.05470414 -1.5566561
## 5  0.8854889  2.2774174  2.6499031  2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267  0.1311945  0.4321289 -2.9622040 -2.55387473  2.6396458
##       stat35     stat36     stat37     stat38     stat39     stat40
## 1  2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006  1.1749642
## 2  1.3709245 -1.3714181  1.3901204  1.2273489 -0.8934880  1.0540369
## 3 -0.9805572  2.0571353  0.8845031  2.0574493  1.1222047  1.8528618
## 4  1.0969149 -2.2820673  1.8852408  0.5391517  2.7334342 -0.4372566
## 5 -1.0971669  1.4867796 -2.3738465 -0.3743561  1.4266498  1.2551680
## 6  0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799  2.0009417
##       stat41     stat42     stat43     stat44     stat45      stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793  0.6298335 -2.09296608
## 2  2.5380247  1.6476108 0.44128850 -2.5049477  1.2726039  1.72492969
## 3  1.1477574  0.2288794 0.08891252  2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582  0.1686397 -2.1591296  1.60828602
## 5  0.2257536  1.9542116 2.66429019  0.8026123 -1.5521187  1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086  1.0057797 -1.50653386
##       stat47     stat48     stat49     stat50     stat51     stat52
## 1 -2.8318939  2.1445766  0.5668035  0.1544579  0.6291955  2.2197027
## 2 -0.5804687 -1.3689737  1.4908396  1.2465997  0.8896304 -2.6024318
## 3  0.7918019  1.5712964  1.1038082 -0.2545658 -2.1662638  0.2660159
## 4 -1.8894132  0.5680230 -0.7023218 -0.3972188  0.1578027  2.1770194
## 5  2.1088455 -2.7195437  2.1961412 -0.2615084  1.2109556  0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435  0.6958785
##      stat53     stat54      stat55     stat56     stat57      stat58
## 1  2.176805  0.5546907 -2.19704103 -0.2884173  1.3232913 -1.32824039
## 2 -2.107441  1.3864788  0.08781975  1.9998228  0.8014438 -0.26979154
## 3  1.234197  2.1337581  1.65231645 -0.4388691 -0.1811156  2.11277962
## 4  2.535406 -2.1387620  0.12856023 -1.9906180  0.9626449  1.65232646
## 5 -2.457080  2.1633499  0.60441124  2.5449364 -1.4978440  2.60542655
## 6  2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
##        stat59     stat60    stat61      stat62     stat63     stat64
## 1  1.24239659 -2.5798278  1.327928  1.68560362  0.6284891 -1.6798652
## 2  0.06379301  0.9465770  1.116928  0.03128772 -2.1944375  0.3382609
## 3  0.93223447  2.4597080  0.465251 -1.71033382 -0.5156728  1.8276784
## 4 -0.29840910  0.7273473 -2.313066 -1.47696018  2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305  0.17813617  2.8956099  2.9411416
## 6 -1.01793981  0.2817057  2.228023 -0.86494124 -0.9747949 -0.1569053
##       stat65     stat66     stat67     stat68     stat69    stat70
## 1 -2.9490898 -0.3325469  1.5745990 -2.2978280  1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002  0.2658547 -1.9616533  2.506130
## 3 -0.2231264 -0.4503301  0.7932286 -1.2453773 -2.2309763  2.309761
## 4 -0.3522418 -2.0720532  0.9442933  2.9212906  0.5100371 -2.441108
## 5 -2.1648991  1.2002029  2.8266985  0.7461294  1.6772674 -1.280000
## 6 -2.2295458  1.1446493  0.2024925 -0.2983998 -2.8203752  1.224030
##       stat71     stat72     stat73     stat74      stat75     stat76
## 1  1.0260956  2.1071210  2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2  0.3525076  1.6922342 -1.2167022 -1.7271879  2.21176434  1.9329683
## 3 -2.1799035 -2.2645276  0.1415582  0.9887453  1.95592320  0.2951785
## 4 -2.4051409  2.0876484 -0.8632146  0.4011389 -1.16986716 -1.2391174
## 5  1.3538754 -0.8089395 -0.5122626 -2.1696892  1.07344925  2.6696169
## 6 -2.8073371 -1.4450488  0.5481212 -1.4381690  0.80917043 -0.1365944
##       stat77      stat78      stat79     stat80     stat81     stat82
## 1 -2.5631845 -2.40331340  0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085  0.38400793  1.80483031 -0.8387642  0.7624431  0.9936900
## 3  1.6757870 -1.81900752  2.70904708 -0.3201959  2.5754235  1.6346260
## 4 -2.1012006 -2.24691081  1.78056848  1.0323739  1.0762523  2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6  1.6143972  0.03233746  2.90835762  1.4000487  2.9275615 -2.8503830
##       stat83     stat84     stat85     stat86    stat87     stat88
## 1  1.2840565 -2.6794965  1.3956039 -1.5290235  2.221152  2.3794982
## 2 -0.2380048  1.9314318 -1.6747955 -0.3663656  1.582659 -0.5222489
## 3 -0.9150769 -1.5520337  2.4186287  2.7273662  1.306642  0.1320062
## 4 -2.5824408 -2.7775943  0.5085060  0.4689015  2.053348  0.7957955
## 5 -1.0017741 -0.2009138  0.3770109  2.4335201 -1.118058  1.3953410
## 6  2.4891765  2.9931953 -1.4171852  0.3905659 -1.856119 -2.1690490
##       stat89     stat90     stat91      stat92     stat93     stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891  2.6002395  1.8890998
## 2  0.9982028 -1.2382015 -0.1574496  0.41086048 -0.5412626 -0.2421387
## 3  0.5956759  1.6871066  2.2452753  2.74279594 -1.5860478  2.9393122
## 4  2.0902634  2.1752586 -2.0677712 -2.37861037  1.1653302  0.1500632
## 5  2.9820614  0.8111660 -0.7842287  0.03766387 -1.1681970  2.1217251
## 6 -1.7428021  0.1579032  1.7456742 -0.36858466 -0.1304616 -1.4555819
##       stat95     stat96      stat97     stat98     stat99   stat100
## 1 -2.6056035 -0.5814857  2.57652426 -2.3297751  2.6324007  1.445827
## 2 -2.0271583 -0.9126074  2.49582648  0.9745382  1.1339203 -2.549544
## 3  0.3823181 -0.6324139  2.46221566  1.1151560  0.4624891  0.107072
## 4  2.6414623 -0.6630505  2.10394859  1.2627635  0.4861740  1.697012
## 5  1.4642254  2.6485956 -0.07699547  0.6219473 -1.8815142 -2.685463
## 6  1.8937331 -0.4690555  1.04671776 -0.5879866 -0.9766789  2.405940
##      stat101   stat102    stat103    stat104    stat105    stat106
## 1 -2.1158021  2.603936  1.7745128 -1.8903574 -1.8558655  1.0122044
## 2 -2.7998588 -2.267895  0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3  0.7969509 -1.744906 -0.7960327  1.9767258 -0.2007264 -0.7872376
## 4  1.7071959 -1.540221  1.6770362  1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983  2.4376490  0.2731541  1.5275587  1.3256483
## 6  2.6888530  1.090155  2.0769854  1.9615480  1.8689761  2.8975825
##     stat107    stat108   stat109    stat110    stat111    stat112
## 1  1.954508 -0.3376471  2.503084  0.3099165  2.7209847 -2.3911204
## 2 -2.515160  0.3998704 -1.077093  2.4228268 -0.7759693  0.2513882
## 3  1.888827  1.5819857 -2.066659 -2.0008364  0.6997684  2.6157095
## 4  1.076395 -1.8524148 -2.689204  1.0985872  1.2389493  2.1018629
## 5  2.828866 -1.8590252 -2.424163  1.4391942 -0.6173239 -1.5218846
## 6 -1.419639  0.7888914  1.996463  0.9813507  0.9034198  1.3810679
##     stat113    stat114    stat115     stat116   stat117    stat118
## 1 -1.616161  1.0878664  0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771  1.8683100  0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952  1.7753417  0.90311784 -1.318836 -0.1429040
## 4  2.459229 -0.5584171  0.4419581 -0.09586351  0.595442  0.2883342
## 5 -2.102200  1.6300170 -2.3498287  1.36771894 -1.912202 -0.2563821
## 6 -1.835037  0.6577786 -2.9928374  2.13540316 -1.437299 -0.9570006
##      stat119    stat120    stat121    stat122    stat123     stat124
## 1  2.9222729  1.9151262  1.6686068  2.0061224  1.5723072  0.78819227
## 2  2.1828208  0.8283178 -2.4458632  1.7133740  1.1393738 -0.07182054
## 3  0.9721319  1.2723130  2.8002086  2.7670381 -2.2252586  2.17499113
## 4 -1.9327896 -2.5369370  1.7835028  1.0262097 -1.8790983 -0.43639564
## 5  1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6  0.1720700 -1.4568460  1.4115051 -0.9878145  2.3895061 -2.33730745
##     stat125    stat126    stat127   stat128    stat129     stat130
## 1  1.588372  1.1620011 -0.2474264  1.650328  2.5147598  0.37283245
## 2 -1.173771  0.8162020  0.3510315 -1.263667  1.7245284 -0.72852904
## 3 -1.503497 -0.5656394  2.8040256 -2.139287 -1.7221642  2.17899609
## 4  1.040967 -2.9039600  0.3103742  1.462339 -1.2940350 -2.95015502
## 5 -2.866184  1.6885070 -2.2525666 -2.628631  1.8581577  2.80127025
## 6 -1.355111  1.5017927  0.4295921 -0.580415  0.9851009 -0.03773117
##       stat131    stat132    stat133    stat134    stat135      stat136
## 1 -0.09028241  0.5194538  2.8478346  2.6664724 -2.0206311  1.398415090
## 2 -0.53045595  1.4134049  2.9180586  0.3299096  1.4784122 -1.278896090
## 3  1.35843194  0.2279946  0.3532595  0.6138676 -0.3443284  0.057763811
## 4 -1.92450273  1.2698178 -1.5299660 -2.6083462  1.1665530 -0.187791914
## 5  1.49036849  2.6337729 -2.3206244  0.4978287 -1.7397571  0.001200184
## 6 -0.64642709 -1.9256228  1.7032650 -0.9152725 -0.3188055  2.155395980
##      stat137    stat138    stat139    stat140    stat141    stat142
## 1 -1.2794871  0.4064890 -0.4539998  2.6660173 -1.8375313  0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910  0.2572918  0.6612002  1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666  2.4969513 -0.9477230
## 4 -1.2318919  2.2348571  0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5  1.8685058  2.7229517 -2.9077182  2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
##     stat143    stat144    stat145    stat146     stat147    stat148
## 1 1.9466263  2.2689433 -0.3597288 -0.6551386  1.65438592  0.6404466
## 2 1.3156421  2.4459090 -0.3790028  1.4858465 -0.07784461  1.0096149
## 3 0.1959563  2.3062942  1.8459278  2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508  2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758  1.8160636  2.8257299 -1.4526173  1.60679603  2.3807991
## 6 0.7623450  0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
##      stat149   stat150    stat151    stat152    stat153    stat154
## 1  0.1583575 0.4755351  0.3213410  2.0241520  1.5720103 -0.1825875
## 2 -0.4311406 2.9577663  0.6937252  0.1397280  0.3775735 -1.1012636
## 3 -0.8352824 2.5716205  1.7528236  0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509  2.3358023  0.1015632  1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471  2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
##     stat155     stat156    stat157    stat158     stat159   stat160
## 1 -1.139657  0.07061254  0.5893906 -1.9920996 -2.83714366  2.249398
## 2 -2.041093  0.74047768  2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693  0.4046920  1.2098547 -0.90412390 -1.934093
## 4  2.992191  2.33222485  2.0622969 -0.6714653  2.76836085 -1.431120
## 5 -2.362356 -1.23906672  0.4746319 -0.7849202  0.69399995  2.052411
## 6 -1.604499  1.31051409 -0.5164744  0.6288667  0.07899523 -2.287402
##      stat161    stat162    stat163    stat164    stat165    stat166
## 1  1.7182635 -1.2323593  2.7350423  1.0707235  1.1621544  0.9493989
## 2 -0.6247674  2.6740098  2.8211024  1.5561292 -1.1027147  1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673  1.4261659  0.5294309
## 4  1.7644744  0.1696584  1.2653207  0.6621516  0.9470508  0.1985014
## 5 -1.2070210  0.7243784  0.9736322  2.7426259 -2.6862383  1.6840212
## 6  2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
##      stat167    stat168    stat169     stat170     stat171    stat172
## 1  0.1146510  2.3872008  1.1180918 -0.95370555 -2.25076509  0.2348182
## 2  1.0760417 -2.0449336  0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3  1.1735898  1.3860190 -2.2894719  0.06350347  0.29191551 -1.6079744
## 4  2.5511832  0.5446648  1.2694012 -0.84571201  0.79789722  0.2623538
## 5  2.2900002  2.6289782 -0.2783571  1.39032829 -0.55532032  1.0499046
## 6 -0.7513983  2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
##       stat173   stat174     stat175    stat176     stat177    stat178
## 1  1.79366076 -1.920206 -0.38841942  0.8530301  1.64532077 -1.1354179
## 2 -0.07484659  1.337846  2.20911694  0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741  0.06148359  2.3066039 -0.34688616  1.1840581
## 4  0.31460321  1.195741  2.97633862  1.1685091 -0.06346265  1.4205489
## 5 -1.39428365  2.458523  0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6  2.31844401  1.239864 -2.06490874  0.7696204 -1.77586019  2.0855925
##      stat179    stat180     stat181    stat182    stat183    stat184
## 1  2.0018647  0.1476815 -1.27279520  1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449  2.9012582 -1.09914911 -2.5488517 -2.8377736  1.4073374
## 3 -1.7819908  2.9902627  0.81908613  0.2503852  0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421  1.3096315  2.1458161 -1.5228094
## 5 -2.2298794  2.4465680 -0.70346898 -1.6997617  2.9178164 -0.3615532
## 6 -1.1168108  1.5552123 -0.01361342  1.7338791 -1.1104763  0.1882416
##      stat185    stat186   stat187    stat188    stat189    stat190
## 1 -0.1043832 -1.5047463  2.700351 -2.4780862 -1.9078265  0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3  2.6727278  1.2688179 -1.399018 -2.9612138  2.6456394  2.0073323
## 4 -2.7796295  2.0682354  2.243727  0.4296881  0.1931333  2.2710960
## 5 -0.6231265  2.5833981  2.229041  0.8139584  1.4544131  1.8886451
## 6  2.7204690 -2.4469144 -1.421998  1.7477882 -0.1481806  0.6011560
##      stat191    stat192    stat193    stat194   stat195    stat196
## 1 -0.6644351  2.6270833 -1.1094601 -2.4200392  2.870713 -0.6590932
## 2 -0.6483142  1.4519118 -0.1963493 -2.3025322  1.255608  2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015  0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5  2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581  2.3033464
## 6  0.4437973  0.6599325 -1.4029555 -2.3118258 -1.792232  1.3934380
##       stat197    stat198    stat199    stat200    stat201    stat202
## 1 -0.83056986  0.9550526 -1.7025776 -2.8263099 -0.7023998  0.2272806
## 2 -1.42178249 -1.2471864  2.5723093 -0.0233496 -1.8975239  1.9472262
## 3 -0.19233958 -0.5161456  0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902  1.4728470 -0.4562025 -2.2983441 -1.5101184  0.2530525
## 5  1.85018563 -1.8269292 -0.6337969 -2.1473246  0.9909850  1.0950903
## 6 -0.09311061  0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
##        stat203      stat204    stat205    stat206    stat207    stat208
## 1  1.166631220  0.007453276  2.9961641  1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504  2.132749800  0.3707606  1.5604026 -1.0089217  2.1474257
## 3  0.003180946  2.229793310  2.7354040  0.8992231  2.9694967  2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255  0.8032548
## 5  2.349412920 -1.276320220 -2.0203719 -1.1733509  1.0371852 -2.5086207
## 6  0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488  1.7993879
##      stat209    stat210   stat211    stat212    stat213    stat214
## 1 -2.2182105 -1.4099774 -1.656754  2.6602585 -2.9270992  1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513  2.9523673  2.0266318
## 3 -1.8279589  0.0472350 -2.026734  2.5054367  0.9903042  0.3274105
## 4 -1.0878067  0.1171303  2.645891 -1.6775225  1.3452160  1.4694063
## 5 -0.8158175  0.4060950  0.912256  0.2925677  2.1610141  0.5679936
## 6 -2.2664354 -0.2061083 -1.435174  2.6645632  0.4216259 -0.6419122
##      stat215    stat216    stat217
## 1 -2.7510750 -0.5501796  1.2638469
## 2  2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676  1.2852937  1.5411530
## 4  0.6343777  0.1345372  2.9102673
## 5  0.9908702  1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912  0.2765074
head(features.highprec)
##     JobName        x1       x2       x3        x4       x5        x6
## 1 Job_00001 2.0734508 4.917267 19.96188  3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
##         x7        x8       x9       x10          x11      x12       x13
## 1 2.979479  8.537228 1.103368 4.6089458 1.050025e-07 7.995825 13.215498
## 2 2.388119  6.561461 0.588572 1.0283282 1.034518e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.062312e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.471887e-08 9.548698 17.170635
## 5 2.891535  9.362389 1.246039 1.7333300 1.010552e-07 9.596095  5.794567
## 6 1.247838  7.033354 1.852231 0.4839371 1.071662e-07 3.810983 23.863169
##        x14       x15       x16      x17      x18      x19      x20
## 1 4.377983 0.2370623  6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121  6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445  8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830  7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382  7.062051 1.341864 7.748325 5.009365 1.725179
##        x21      x22      x23      stat1      stat2      stat3      stat4
## 1 42.36548 1.356213 2.699796  2.3801832  0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480  1.8140973  1.6204884  2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289  1.1415752  2.9805934
## 4 32.60415 1.355396 3.402398  0.4371202 -1.9355906  0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011  2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084  1.8853619  2.4154840 -2.6022179
##          stat5      stat6      stat7      stat8      stat9      stat10
## 1  0.148893163 -0.6622978 -2.4851868  0.3647782  2.5364335  2.92067981
## 2  1.920768980  1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3  2.422584300 -0.4166040  2.2205689 -2.6741531  0.4844292  2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467  1.7960306  0.74771154
## 5 -2.311132430 -1.0166832  2.7269876  1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915  1.4875095  2.0188572 -1.4892503 -1.41103566
##       stat11    stat12     stat13     stat14     stat15     stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812  0.6029300
## 2 -0.8547903  1.119316  0.7227427  0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580  2.865401 -2.9756081  2.9871745  1.9539525 -1.8857163
## 4  1.3982378  1.856765 -1.0379983  2.3341896  2.3057184 -2.8947697
## 5  0.9567220  2.567549  0.3184886  1.0307668  0.1644241 -0.6613821
## 6  0.5341771 -1.461822  0.4402476 -1.9282095 -0.3680157  1.8188807
##        stat17     stat18     stat19     stat20     stat21     stat22
## 1 -1.04516208  2.3544915  2.4049001  0.2633883 -0.9788178  1.7868229
## 2  0.09513074  0.4727738  1.8899702  2.7892542 -1.3919091 -1.7198164
## 3  0.40285346  1.4655282 -1.4952788  2.9162340 -2.3893208  2.8161423
## 4  2.97446084  2.3896182  2.3083484 -1.1894441 -2.1982553  1.3666242
## 5 -0.98465055  0.6900643  1.5894209 -2.1204538  1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904  1.1764175 -2.9100556 -2.1359294
##       stat23     stat24     stat25    stat26     stat27      stat28
## 1 -2.3718851  2.8580718 -0.4719713 -2.817086 -0.9518474  2.88892484
## 2 -2.3293245  1.5577759 -1.9569720  1.554194 -0.5081459 -1.58715141
## 3 -2.5402296  0.1422861  0.3572798 -1.051886 -2.1541717  0.03074004
## 4 -1.9679050 -1.4077642  2.5097435  1.683121 -0.2549745 -2.90384054
## 5  2.0523429 -2.2084844 -1.9280857 -2.116736  1.8180779 -1.42167580
## 6  0.2184991 -0.7599817  2.6880329 -2.903350 -1.0733233 -2.92416644
##       stat29     stat30     stat31     stat32      stat33     stat34
## 1  0.7991088 -2.0059092 -0.2461502  0.6482101 -2.87462163 -0.3601543
## 2  1.9758110 -0.3874187  1.3566630  2.6493473  2.28463054  1.8591728
## 3 -0.4460218  1.0279679  1.3998452 -1.0183365  1.41109037 -2.4183984
## 4  1.0571996  2.5588036 -2.9830337 -1.1299983  0.05470414 -1.5566561
## 5  0.8854889  2.2774174  2.6499031  2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267  0.1311945  0.4321289 -2.9622040 -2.55387473  2.6396458
##       stat35     stat36     stat37     stat38     stat39     stat40
## 1  2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006  1.1749642
## 2  1.3709245 -1.3714181  1.3901204  1.2273489 -0.8934880  1.0540369
## 3 -0.9805572  2.0571353  0.8845031  2.0574493  1.1222047  1.8528618
## 4  1.0969149 -2.2820673  1.8852408  0.5391517  2.7334342 -0.4372566
## 5 -1.0971669  1.4867796 -2.3738465 -0.3743561  1.4266498  1.2551680
## 6  0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799  2.0009417
##       stat41     stat42     stat43     stat44     stat45      stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793  0.6298335 -2.09296608
## 2  2.5380247  1.6476108 0.44128850 -2.5049477  1.2726039  1.72492969
## 3  1.1477574  0.2288794 0.08891252  2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582  0.1686397 -2.1591296  1.60828602
## 5  0.2257536  1.9542116 2.66429019  0.8026123 -1.5521187  1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086  1.0057797 -1.50653386
##       stat47     stat48     stat49     stat50     stat51     stat52
## 1 -2.8318939  2.1445766  0.5668035  0.1544579  0.6291955  2.2197027
## 2 -0.5804687 -1.3689737  1.4908396  1.2465997  0.8896304 -2.6024318
## 3  0.7918019  1.5712964  1.1038082 -0.2545658 -2.1662638  0.2660159
## 4 -1.8894132  0.5680230 -0.7023218 -0.3972188  0.1578027  2.1770194
## 5  2.1088455 -2.7195437  2.1961412 -0.2615084  1.2109556  0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435  0.6958785
##      stat53     stat54      stat55     stat56     stat57      stat58
## 1  2.176805  0.5546907 -2.19704103 -0.2884173  1.3232913 -1.32824039
## 2 -2.107441  1.3864788  0.08781975  1.9998228  0.8014438 -0.26979154
## 3  1.234197  2.1337581  1.65231645 -0.4388691 -0.1811156  2.11277962
## 4  2.535406 -2.1387620  0.12856023 -1.9906180  0.9626449  1.65232646
## 5 -2.457080  2.1633499  0.60441124  2.5449364 -1.4978440  2.60542655
## 6  2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
##        stat59     stat60    stat61      stat62     stat63     stat64
## 1  1.24239659 -2.5798278  1.327928  1.68560362  0.6284891 -1.6798652
## 2  0.06379301  0.9465770  1.116928  0.03128772 -2.1944375  0.3382609
## 3  0.93223447  2.4597080  0.465251 -1.71033382 -0.5156728  1.8276784
## 4 -0.29840910  0.7273473 -2.313066 -1.47696018  2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305  0.17813617  2.8956099  2.9411416
## 6 -1.01793981  0.2817057  2.228023 -0.86494124 -0.9747949 -0.1569053
##       stat65     stat66     stat67     stat68     stat69    stat70
## 1 -2.9490898 -0.3325469  1.5745990 -2.2978280  1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002  0.2658547 -1.9616533  2.506130
## 3 -0.2231264 -0.4503301  0.7932286 -1.2453773 -2.2309763  2.309761
## 4 -0.3522418 -2.0720532  0.9442933  2.9212906  0.5100371 -2.441108
## 5 -2.1648991  1.2002029  2.8266985  0.7461294  1.6772674 -1.280000
## 6 -2.2295458  1.1446493  0.2024925 -0.2983998 -2.8203752  1.224030
##       stat71     stat72     stat73     stat74      stat75     stat76
## 1  1.0260956  2.1071210  2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2  0.3525076  1.6922342 -1.2167022 -1.7271879  2.21176434  1.9329683
## 3 -2.1799035 -2.2645276  0.1415582  0.9887453  1.95592320  0.2951785
## 4 -2.4051409  2.0876484 -0.8632146  0.4011389 -1.16986716 -1.2391174
## 5  1.3538754 -0.8089395 -0.5122626 -2.1696892  1.07344925  2.6696169
## 6 -2.8073371 -1.4450488  0.5481212 -1.4381690  0.80917043 -0.1365944
##       stat77      stat78      stat79     stat80     stat81     stat82
## 1 -2.5631845 -2.40331340  0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085  0.38400793  1.80483031 -0.8387642  0.7624431  0.9936900
## 3  1.6757870 -1.81900752  2.70904708 -0.3201959  2.5754235  1.6346260
## 4 -2.1012006 -2.24691081  1.78056848  1.0323739  1.0762523  2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6  1.6143972  0.03233746  2.90835762  1.4000487  2.9275615 -2.8503830
##       stat83     stat84     stat85     stat86    stat87     stat88
## 1  1.2840565 -2.6794965  1.3956039 -1.5290235  2.221152  2.3794982
## 2 -0.2380048  1.9314318 -1.6747955 -0.3663656  1.582659 -0.5222489
## 3 -0.9150769 -1.5520337  2.4186287  2.7273662  1.306642  0.1320062
## 4 -2.5824408 -2.7775943  0.5085060  0.4689015  2.053348  0.7957955
## 5 -1.0017741 -0.2009138  0.3770109  2.4335201 -1.118058  1.3953410
## 6  2.4891765  2.9931953 -1.4171852  0.3905659 -1.856119 -2.1690490
##       stat89     stat90     stat91      stat92     stat93     stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891  2.6002395  1.8890998
## 2  0.9982028 -1.2382015 -0.1574496  0.41086048 -0.5412626 -0.2421387
## 3  0.5956759  1.6871066  2.2452753  2.74279594 -1.5860478  2.9393122
## 4  2.0902634  2.1752586 -2.0677712 -2.37861037  1.1653302  0.1500632
## 5  2.9820614  0.8111660 -0.7842287  0.03766387 -1.1681970  2.1217251
## 6 -1.7428021  0.1579032  1.7456742 -0.36858466 -0.1304616 -1.4555819
##       stat95     stat96      stat97     stat98     stat99   stat100
## 1 -2.6056035 -0.5814857  2.57652426 -2.3297751  2.6324007  1.445827
## 2 -2.0271583 -0.9126074  2.49582648  0.9745382  1.1339203 -2.549544
## 3  0.3823181 -0.6324139  2.46221566  1.1151560  0.4624891  0.107072
## 4  2.6414623 -0.6630505  2.10394859  1.2627635  0.4861740  1.697012
## 5  1.4642254  2.6485956 -0.07699547  0.6219473 -1.8815142 -2.685463
## 6  1.8937331 -0.4690555  1.04671776 -0.5879866 -0.9766789  2.405940
##      stat101   stat102    stat103    stat104    stat105    stat106
## 1 -2.1158021  2.603936  1.7745128 -1.8903574 -1.8558655  1.0122044
## 2 -2.7998588 -2.267895  0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3  0.7969509 -1.744906 -0.7960327  1.9767258 -0.2007264 -0.7872376
## 4  1.7071959 -1.540221  1.6770362  1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983  2.4376490  0.2731541  1.5275587  1.3256483
## 6  2.6888530  1.090155  2.0769854  1.9615480  1.8689761  2.8975825
##     stat107    stat108   stat109    stat110    stat111    stat112
## 1  1.954508 -0.3376471  2.503084  0.3099165  2.7209847 -2.3911204
## 2 -2.515160  0.3998704 -1.077093  2.4228268 -0.7759693  0.2513882
## 3  1.888827  1.5819857 -2.066659 -2.0008364  0.6997684  2.6157095
## 4  1.076395 -1.8524148 -2.689204  1.0985872  1.2389493  2.1018629
## 5  2.828866 -1.8590252 -2.424163  1.4391942 -0.6173239 -1.5218846
## 6 -1.419639  0.7888914  1.996463  0.9813507  0.9034198  1.3810679
##     stat113    stat114    stat115     stat116   stat117    stat118
## 1 -1.616161  1.0878664  0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771  1.8683100  0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952  1.7753417  0.90311784 -1.318836 -0.1429040
## 4  2.459229 -0.5584171  0.4419581 -0.09586351  0.595442  0.2883342
## 5 -2.102200  1.6300170 -2.3498287  1.36771894 -1.912202 -0.2563821
## 6 -1.835037  0.6577786 -2.9928374  2.13540316 -1.437299 -0.9570006
##      stat119    stat120    stat121    stat122    stat123     stat124
## 1  2.9222729  1.9151262  1.6686068  2.0061224  1.5723072  0.78819227
## 2  2.1828208  0.8283178 -2.4458632  1.7133740  1.1393738 -0.07182054
## 3  0.9721319  1.2723130  2.8002086  2.7670381 -2.2252586  2.17499113
## 4 -1.9327896 -2.5369370  1.7835028  1.0262097 -1.8790983 -0.43639564
## 5  1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6  0.1720700 -1.4568460  1.4115051 -0.9878145  2.3895061 -2.33730745
##     stat125    stat126    stat127   stat128    stat129     stat130
## 1  1.588372  1.1620011 -0.2474264  1.650328  2.5147598  0.37283245
## 2 -1.173771  0.8162020  0.3510315 -1.263667  1.7245284 -0.72852904
## 3 -1.503497 -0.5656394  2.8040256 -2.139287 -1.7221642  2.17899609
## 4  1.040967 -2.9039600  0.3103742  1.462339 -1.2940350 -2.95015502
## 5 -2.866184  1.6885070 -2.2525666 -2.628631  1.8581577  2.80127025
## 6 -1.355111  1.5017927  0.4295921 -0.580415  0.9851009 -0.03773117
##       stat131    stat132    stat133    stat134    stat135      stat136
## 1 -0.09028241  0.5194538  2.8478346  2.6664724 -2.0206311  1.398415090
## 2 -0.53045595  1.4134049  2.9180586  0.3299096  1.4784122 -1.278896090
## 3  1.35843194  0.2279946  0.3532595  0.6138676 -0.3443284  0.057763811
## 4 -1.92450273  1.2698178 -1.5299660 -2.6083462  1.1665530 -0.187791914
## 5  1.49036849  2.6337729 -2.3206244  0.4978287 -1.7397571  0.001200184
## 6 -0.64642709 -1.9256228  1.7032650 -0.9152725 -0.3188055  2.155395980
##      stat137    stat138    stat139    stat140    stat141    stat142
## 1 -1.2794871  0.4064890 -0.4539998  2.6660173 -1.8375313  0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910  0.2572918  0.6612002  1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666  2.4969513 -0.9477230
## 4 -1.2318919  2.2348571  0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5  1.8685058  2.7229517 -2.9077182  2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
##     stat143    stat144    stat145    stat146     stat147    stat148
## 1 1.9466263  2.2689433 -0.3597288 -0.6551386  1.65438592  0.6404466
## 2 1.3156421  2.4459090 -0.3790028  1.4858465 -0.07784461  1.0096149
## 3 0.1959563  2.3062942  1.8459278  2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508  2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758  1.8160636  2.8257299 -1.4526173  1.60679603  2.3807991
## 6 0.7623450  0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
##      stat149   stat150    stat151    stat152    stat153    stat154
## 1  0.1583575 0.4755351  0.3213410  2.0241520  1.5720103 -0.1825875
## 2 -0.4311406 2.9577663  0.6937252  0.1397280  0.3775735 -1.1012636
## 3 -0.8352824 2.5716205  1.7528236  0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509  2.3358023  0.1015632  1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471  2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
##     stat155     stat156    stat157    stat158     stat159   stat160
## 1 -1.139657  0.07061254  0.5893906 -1.9920996 -2.83714366  2.249398
## 2 -2.041093  0.74047768  2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693  0.4046920  1.2098547 -0.90412390 -1.934093
## 4  2.992191  2.33222485  2.0622969 -0.6714653  2.76836085 -1.431120
## 5 -2.362356 -1.23906672  0.4746319 -0.7849202  0.69399995  2.052411
## 6 -1.604499  1.31051409 -0.5164744  0.6288667  0.07899523 -2.287402
##      stat161    stat162    stat163    stat164    stat165    stat166
## 1  1.7182635 -1.2323593  2.7350423  1.0707235  1.1621544  0.9493989
## 2 -0.6247674  2.6740098  2.8211024  1.5561292 -1.1027147  1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673  1.4261659  0.5294309
## 4  1.7644744  0.1696584  1.2653207  0.6621516  0.9470508  0.1985014
## 5 -1.2070210  0.7243784  0.9736322  2.7426259 -2.6862383  1.6840212
## 6  2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
##      stat167    stat168    stat169     stat170     stat171    stat172
## 1  0.1146510  2.3872008  1.1180918 -0.95370555 -2.25076509  0.2348182
## 2  1.0760417 -2.0449336  0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3  1.1735898  1.3860190 -2.2894719  0.06350347  0.29191551 -1.6079744
## 4  2.5511832  0.5446648  1.2694012 -0.84571201  0.79789722  0.2623538
## 5  2.2900002  2.6289782 -0.2783571  1.39032829 -0.55532032  1.0499046
## 6 -0.7513983  2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
##       stat173   stat174     stat175    stat176     stat177    stat178
## 1  1.79366076 -1.920206 -0.38841942  0.8530301  1.64532077 -1.1354179
## 2 -0.07484659  1.337846  2.20911694  0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741  0.06148359  2.3066039 -0.34688616  1.1840581
## 4  0.31460321  1.195741  2.97633862  1.1685091 -0.06346265  1.4205489
## 5 -1.39428365  2.458523  0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6  2.31844401  1.239864 -2.06490874  0.7696204 -1.77586019  2.0855925
##      stat179    stat180     stat181    stat182    stat183    stat184
## 1  2.0018647  0.1476815 -1.27279520  1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449  2.9012582 -1.09914911 -2.5488517 -2.8377736  1.4073374
## 3 -1.7819908  2.9902627  0.81908613  0.2503852  0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421  1.3096315  2.1458161 -1.5228094
## 5 -2.2298794  2.4465680 -0.70346898 -1.6997617  2.9178164 -0.3615532
## 6 -1.1168108  1.5552123 -0.01361342  1.7338791 -1.1104763  0.1882416
##      stat185    stat186   stat187    stat188    stat189    stat190
## 1 -0.1043832 -1.5047463  2.700351 -2.4780862 -1.9078265  0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3  2.6727278  1.2688179 -1.399018 -2.9612138  2.6456394  2.0073323
## 4 -2.7796295  2.0682354  2.243727  0.4296881  0.1931333  2.2710960
## 5 -0.6231265  2.5833981  2.229041  0.8139584  1.4544131  1.8886451
## 6  2.7204690 -2.4469144 -1.421998  1.7477882 -0.1481806  0.6011560
##      stat191    stat192    stat193    stat194   stat195    stat196
## 1 -0.6644351  2.6270833 -1.1094601 -2.4200392  2.870713 -0.6590932
## 2 -0.6483142  1.4519118 -0.1963493 -2.3025322  1.255608  2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015  0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5  2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581  2.3033464
## 6  0.4437973  0.6599325 -1.4029555 -2.3118258 -1.792232  1.3934380
##       stat197    stat198    stat199    stat200    stat201    stat202
## 1 -0.83056986  0.9550526 -1.7025776 -2.8263099 -0.7023998  0.2272806
## 2 -1.42178249 -1.2471864  2.5723093 -0.0233496 -1.8975239  1.9472262
## 3 -0.19233958 -0.5161456  0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902  1.4728470 -0.4562025 -2.2983441 -1.5101184  0.2530525
## 5  1.85018563 -1.8269292 -0.6337969 -2.1473246  0.9909850  1.0950903
## 6 -0.09311061  0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
##        stat203      stat204    stat205    stat206    stat207    stat208
## 1  1.166631220  0.007453276  2.9961641  1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504  2.132749800  0.3707606  1.5604026 -1.0089217  2.1474257
## 3  0.003180946  2.229793310  2.7354040  0.8992231  2.9694967  2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255  0.8032548
## 5  2.349412920 -1.276320220 -2.0203719 -1.1733509  1.0371852 -2.5086207
## 6  0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488  1.7993879
##      stat209    stat210   stat211    stat212    stat213    stat214
## 1 -2.2182105 -1.4099774 -1.656754  2.6602585 -2.9270992  1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513  2.9523673  2.0266318
## 3 -1.8279589  0.0472350 -2.026734  2.5054367  0.9903042  0.3274105
## 4 -1.0878067  0.1171303  2.645891 -1.6775225  1.3452160  1.4694063
## 5 -0.8158175  0.4060950  0.912256  0.2925677  2.1610141  0.5679936
## 6 -2.2664354 -0.2061083 -1.435174  2.6645632  0.4216259 -0.6419122
##      stat215    stat216    stat217
## 1 -2.7510750 -0.5501796  1.2638469
## 2  2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676  1.2852937  1.5411530
## 4  0.6343777  0.1345372  2.9102673
## 5  0.9908702  1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912  0.2765074
features = features.highprec
#str(features) 

Checking correlations to evaluate removal of redundant features

corr.matrix = round(cor(features[sapply(features, is.numeric)]),2)

# filter out only highly correlated variables
threshold = 0.6
corr.matrix.tmp = corr.matrix
diag(corr.matrix.tmp) = 0
high.corr = apply(abs(corr.matrix.tmp) >= threshold, 1, any)
high.corr.matrix = corr.matrix.tmp[high.corr, high.corr]

DT::datatable(corr.matrix)
DT::datatable(high.corr.matrix)

Feature Names

feature.names = colnames(features)
drops <- c('JobName')
feature.names = feature.names[!(feature.names %in% drops)]
#str(feature.names)

Read and Clean Labels

labels = read.csv("../../Data/labels.csv")
#str(labels)
labels = labels[,c("JobName", output.var)]
summary(labels)
##       JobName           y3        
##  Job_00001:   1   Min.   : 95.91  
##  Job_00002:   1   1st Qu.:118.21  
##  Job_00003:   1   Median :123.99  
##  Job_00004:   1   Mean   :125.36  
##  Job_00005:   1   3rd Qu.:131.06  
##  Job_00006:   1   Max.   :193.73  
##  (Other)  :9994   NA's   :2497

Merge Datasets

data <- merge(features, labels, by = 'JobName')
drops <- c('JobName')
data = data[,(!colnames(data) %in% drops)]
#str(data)

Transformations

if (transform.abs == TRUE){
  data[,label.names] = 10^(data[,label.names]/20)
  data = filter(data, y3 < 1E7)
}


#str(data)
if (log.pred == TRUE){
  data[label.names] = log(data[alt.scale.label.name],10)
  
  drops = c(alt.scale.label.name)
  data = data[!(names(data) %in% drops)]
}
#str(data)

Remove NA Cases

data = data[complete.cases(data),]

Exploratory Data Analysis

Check correlation of Label with Featires

if (eda == TRUE){
  corr.to.label =round(cor(dplyr::select(data,-one_of(label.names)),dplyr::select_at(data,label.names)),4)
  DT::datatable(corr.to.label)
}

Multicollinearity - VIF

if (eda == TRUE){
  vifDF = usdm::vif(select_at(data,feature.names)) %>% arrange(desc(VIF))
  head(vifDF,10)
}
##    Variables      VIF
## 1    stat131 1.065226
## 2    stat113 1.063307
## 3    stat202 1.062843
## 4     stat72 1.062697
## 5    stat142 1.062557
## 6     stat31 1.060336
## 7     stat80 1.059814
## 8    stat147 1.059225
## 9    stat140 1.059145
## 10     stat8 1.059094

Scatterplots

panel.hist <- function(x, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(usr[1:2], 0, 1.5) )
    h <- hist(x, plot = FALSE)
    breaks <- h$breaks; nB <- length(breaks)
    y <- h$counts; y <- y/max(y)
    rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}
if (eda == TRUE){
  histogram(data[ ,label.names])
  #hist(data[complete.cases(data),alt.scale.label.name])
}

# https://stackoverflow.com/questions/24648729/plot-one-numeric-variable-against-n-numeric-variables-in-n-plots
ind.pairs.plot <- function(data, xvars=NULL, yvar)
{
    df <- data
    if (is.null(xvars)) {
        xvars = names(data[which(names(data)!=yvar)])       
    }   

    #choose a format to display charts
    ncharts <- length(xvars) 
    
    for(i in 1:ncharts){    
        plot(df[,xvars[i]],df[,yvar], xlab = xvars[i], ylab = yvar)
    }
}

if (eda == TRUE){
  ind.pairs.plot(data, feature.names, label.names)
}

# 
# pl <- ggplot(data, aes(x=x18, y = y3))
# pl2 <- pl + geom_point(aes(alpha = 0.1)) # default color gradient based on 'hp'
# print(pl2)

Feature Engineering

if(eda ==FALSE){
  # x18 may need transformations
  plot(data[,'x18'], data[,label.names], main = "Original Scatter Plot vs. x18", ylab = label.names, xlab = 'x18')
  plot(sqrt(data[,'x18']), data[,label.names], main = "Original Scatter Plot vs. sqrt(x18)", ylab = label.names, xlab = 'sqrt(x18)')
  
  # transforming x18
  data$sqrt.x18 = sqrt(data$x18)
  data = dplyr::select(data,-one_of('x18'))
  
  # what about x7, x9?
  # x11 looks like data is at discrete points after a while. Will this be a problem?
}

Modeling

Train Test Split

data = data[sample(nrow(data)),] # randomly shuffle data
split = sample.split(data[,label.names], SplitRatio = 0.8)

data.train = subset(data, split == TRUE)
data.test = subset(data, split == FALSE)

Common Functions

plot.diagnostics <-  function(model, train) {
  plot(model)
  
  residuals = resid(model) # Plotted above in plot(lm.out)
  r.standard = rstandard(model)
  r.student = rstudent(model)

  plot(predict(model,train),r.student,
      ylab="Student Residuals", xlab="Predicted Values", 
      main="Student Residual Plot") 
  abline(0, 0)
  
  plot(predict(model, train),r.standard,
      ylab="Standard Residuals", xlab="Predicted Values", 
      main="Standard Residual Plot") 
  abline(0, 0)
  abline(2, 0)
  abline(-2, 0)
  
  # Histogram
  hist(r.student, freq=FALSE, main="Distribution of Studentized Residuals", 
  xlab="Studentized Residuals", ylab="Density", ylim=c(0,0.5))

  # Create range of x-values for normal curve
  xfit <- seq(min(r.student)-1, max(r.student)+1, length=40)

  # Generate values from the normal distribution at the specified values
  yfit <- (dnorm(xfit))

  # Add the normal curve
  lines(xfit, yfit, ylim=c(0,0.5))
  
  
  # http://www.stat.columbia.edu/~martin/W2024/R7.pdf
  # Influential plots
  inf.meas = influence.measures(model)
  # print (summary(inf.meas)) # too much data
  
  # Leverage plot
  lev = hat(model.matrix(model))
  plot(lev, ylab = 'Leverage - check')
  
  # Cook's Distance
  cd = cooks.distance(model)
  plot(cd,ylab="Cooks distances")
  abline(4/nrow(train),0)
  abline(1,0)
  
  print (paste("Number of data points that have Cook's D > 4/n: ", length(cd[cd > 4/nrow(train)]), sep = "")) 
  print (paste("Number of data points that have Cook's D > 1: ", length(cd[cd > 1]), sep = "")) 
  return(cd)
}

train.caret.glmselect = function(formula, data, method
                                 ,subopt = NULL, feature.names
                                 , train.control = NULL, tune.grid = NULL, pre.proc = NULL){
  
  if(is.null(train.control)){
    train.control <- trainControl(method = "cv"
                              ,number = 10
                              ,search = "grid"
                              ,verboseIter = TRUE
                              ,allowParallel = TRUE
                              )
  }
  
  if(is.null(tune.grid)){
    if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
      tune.grid = data.frame(nvmax = 1:length(feature.names))
    }
    if (method == 'glmnet' && subopt == 'LASSO'){
      # Will only show 1 Lambda value during training, but that is OK
      # https://stackoverflow.com/questions/47526544/why-need-to-tune-lambda-with-carettrain-method-glmnet-and-cv-glmnet
      # Another option for LASSO is this: https://github.com/topepo/caret/blob/master/RegressionTests/Code/lasso.R
      lambda = 10^seq(-2,0, length =100)
      alpha = c(1)
      tune.grid = expand.grid(alpha = alpha,lambda = lambda)
    }
    if (method == 'lars'){
      # https://github.com/topepo/caret/blob/master/RegressionTests/Code/lars.R
      fraction = seq(0, 1, length = 100)
      tune.grid = expand.grid(fraction = fraction)
      pre.proc = c("center", "scale") 
    }
  }
  
  # http://sshaikh.org/2015/05/06/parallelize-machine-learning-in-r-with-multi-core-cpus/
  cl <- makeCluster(detectCores()*0.75) # use 75% of cores only, leave rest for other tasks
  registerDoParallel(cl)

  set.seed(1) 
  # note that the seed has to actually be set just before this function is called
  # settign is above just not ensure reproducibility for some reason
  model.caret <- caret::train(formula
                              , data = data
                              , method = method
                              , tuneGrid = tune.grid
                              , trControl = train.control
                              , preProc = pre.proc
                              )
  
  stopCluster(cl)
  registerDoSEQ() # register sequential engine in case you are not using this function anymore
  
  if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
    print(model.caret$results) # all model results
    print(model.caret$bestTune) # best model
  
    model = model.caret$finalModel
  
    # Provides the coefficients of the best model
    id = rownames(model.caret$bestTune)
    message("Coefficients of final model:")
    print (coef(model, id = id))
    
    # Need to find alternate to plotting diagnostic plots
    # plot.diagnostics(model.forward,data.train)
    # plot(model.forward,labels = colnames(data.train),scale=c("bic")) ## too many variables
    return(list(model = model,id = id))
  }
  if (method == 'glmnet' && subopt == 'LASSO'){
    print(model.caret)
    print(plot(model.caret))
    print(model.caret$bestTune)
    id = NULL # not really needed but added for consistency
    return(list(model = model.caret,id = id))
  }
  if (method == 'lars'){
    print(model.caret)
    print(plot(model.caret))
    print(model.caret$bestTune)
    id = NULL # not really needed but added for consistency
    return(list(model = model.caret,id = id))
  }
}

# https://stackoverflow.com/questions/48265743/linear-model-subset-selection-goodness-of-fit-with-k-fold-cross-validation
# changes slightly since call[[2]] was just returning "formula" without actually returnign the value in formula
predict.regsubsets <- function(object, newdata, id, formula, ...) {
    #form <- as.formula(object$call[[2]])
    mat <- model.matrix(formula, newdata) # adds intercept and expands any interaction terms
    coefi <- coef(object, id = id)
    xvars <- names(coefi)
    return(mat[,xvars]%*%coefi)
}
  
test.model = function(model, test, level=0.95
                      ,draw.limits = FALSE, good = 0.1, ok = 0.15
                      ,method = NULL, subopt = NULL
                      ,id = NULL, formula, feature.names, label.names){
  ## if using caret for glm select equivalent functionality, 
  ## need to set regsubset = TRUE, pass id of best model through id variable, 
  ## and pass formula (full is ok as it will select subset of variables from there)
  if (is.null(method)){
    pred = predict(model, newdata=test, interval="confidence", level = level) 
  }
  
  if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
    pred = predict.regsubsets(model, newdata = test, id = id, formula = formula)
  }
  
  if (method == 'glmnet' && subopt == 'LASSO'){
    xtest = as.matrix(test[,feature.names]) 
    pred=as.data.frame(predict(model, xtest))
  }
  
  if (method == 'lars'){
    pred=as.data.frame(predict(model, newdata = test))
  }
    
  # Summary of predicted values
  print ("Summary of predicted values: ")
  print(summary(pred[,1]))

  test.mse = mean((test[,label.names]-pred[,1])^2)
  print (paste(method, subopt, "Test MSE:", test.mse, sep=" "))

  plot(test[,label.names],pred[,1],xlab = "Actual", ylab = "Predicted")
  abline(0,(1+good),col='green', lwd = 3)
  abline(0,(1-good),col='green', lwd = 3)
  abline(0,(1+ok),col='blue', lwd = 3)
  abline(0,(1-ok),col='blue', lwd = 3)
  
}

Setup Formulae

n <- names(data.train)
formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~", paste(n[!n %in% label.names], collapse = " + "))) 
grand.mean.formula = as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~ 1"))
print(formula)
## y3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + 
##     x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20 + x21 + 
##     x22 + x23 + stat1 + stat2 + stat3 + stat4 + stat5 + stat6 + 
##     stat7 + stat8 + stat9 + stat10 + stat11 + stat12 + stat13 + 
##     stat14 + stat15 + stat16 + stat17 + stat18 + stat19 + stat20 + 
##     stat21 + stat22 + stat23 + stat24 + stat25 + stat26 + stat27 + 
##     stat28 + stat29 + stat30 + stat31 + stat32 + stat33 + stat34 + 
##     stat35 + stat36 + stat37 + stat38 + stat39 + stat40 + stat41 + 
##     stat42 + stat43 + stat44 + stat45 + stat46 + stat47 + stat48 + 
##     stat49 + stat50 + stat51 + stat52 + stat53 + stat54 + stat55 + 
##     stat56 + stat57 + stat58 + stat59 + stat60 + stat61 + stat62 + 
##     stat63 + stat64 + stat65 + stat66 + stat67 + stat68 + stat69 + 
##     stat70 + stat71 + stat72 + stat73 + stat74 + stat75 + stat76 + 
##     stat77 + stat78 + stat79 + stat80 + stat81 + stat82 + stat83 + 
##     stat84 + stat85 + stat86 + stat87 + stat88 + stat89 + stat90 + 
##     stat91 + stat92 + stat93 + stat94 + stat95 + stat96 + stat97 + 
##     stat98 + stat99 + stat100 + stat101 + stat102 + stat103 + 
##     stat104 + stat105 + stat106 + stat107 + stat108 + stat109 + 
##     stat110 + stat111 + stat112 + stat113 + stat114 + stat115 + 
##     stat116 + stat117 + stat118 + stat119 + stat120 + stat121 + 
##     stat122 + stat123 + stat124 + stat125 + stat126 + stat127 + 
##     stat128 + stat129 + stat130 + stat131 + stat132 + stat133 + 
##     stat134 + stat135 + stat136 + stat137 + stat138 + stat139 + 
##     stat140 + stat141 + stat142 + stat143 + stat144 + stat145 + 
##     stat146 + stat147 + stat148 + stat149 + stat150 + stat151 + 
##     stat152 + stat153 + stat154 + stat155 + stat156 + stat157 + 
##     stat158 + stat159 + stat160 + stat161 + stat162 + stat163 + 
##     stat164 + stat165 + stat166 + stat167 + stat168 + stat169 + 
##     stat170 + stat171 + stat172 + stat173 + stat174 + stat175 + 
##     stat176 + stat177 + stat178 + stat179 + stat180 + stat181 + 
##     stat182 + stat183 + stat184 + stat185 + stat186 + stat187 + 
##     stat188 + stat189 + stat190 + stat191 + stat192 + stat193 + 
##     stat194 + stat195 + stat196 + stat197 + stat198 + stat199 + 
##     stat200 + stat201 + stat202 + stat203 + stat204 + stat205 + 
##     stat206 + stat207 + stat208 + stat209 + stat210 + stat211 + 
##     stat212 + stat213 + stat214 + stat215 + stat216 + stat217
print(grand.mean.formula)
## y3 ~ 1
# Update feature.names because we may have transformed some features
feature.names = n[!n %in% label.names]

Full & Grand Means Model

model.full = lm(formula , data.train)
summary(model.full)
## 
## Call:
## lm(formula = formula, data = data.train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.324  -6.160  -1.630   4.611  56.277 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.464e+01  2.777e+00  34.073  < 2e-16 ***
## x1          -3.514e-02  1.934e-01  -0.182 0.855797    
## x2           1.521e-01  1.239e-01   1.228 0.219683    
## x3           3.693e-02  3.395e-02   1.088 0.276667    
## x4          -1.355e-02  2.671e-03  -5.073 4.03e-07 ***
## x5           1.785e-01  8.750e-02   2.040 0.041435 *  
## x6           2.585e-03  1.762e-01   0.015 0.988295    
## x7           3.303e+00  1.885e-01  17.519  < 2e-16 ***
## x8           1.587e-01  4.400e-02   3.607 0.000312 ***
## x9           9.056e-01  9.865e-02   9.180  < 2e-16 ***
## x10          3.369e-01  9.178e-02   3.670 0.000244 ***
## x11          5.872e+07  2.192e+07   2.680 0.007394 ** 
## x12         -4.622e-02  5.571e-02  -0.830 0.406755    
## x13          2.457e-02  2.218e-02   1.108 0.267863    
## x14         -1.984e-01  9.574e-02  -2.072 0.038304 *  
## x15          2.699e-02  9.096e-02   0.297 0.766654    
## x16          2.459e-01  6.304e-02   3.901 9.68e-05 ***
## x17          4.087e-01  9.613e-02   4.251 2.16e-05 ***
## x18          1.683e+00  6.732e-02  25.005  < 2e-16 ***
## x19          7.622e-02  4.890e-02   1.559 0.119160    
## x20          1.642e-01  3.401e-01   0.483 0.629125    
## x21          4.054e-02  1.252e-02   3.237 0.001214 ** 
## x22         -1.450e-01  1.028e-01  -1.411 0.158289    
## x23          3.588e-03  9.673e-02   0.037 0.970415    
## stat1        2.077e-02  7.353e-02   0.282 0.777644    
## stat2       -1.798e-02  7.354e-02  -0.244 0.806883    
## stat3        1.738e-01  7.345e-02   2.366 0.018006 *  
## stat4       -9.330e-02  7.364e-02  -1.267 0.205231    
## stat5       -1.707e-03  7.351e-02  -0.023 0.981477    
## stat6       -1.337e-03  7.392e-02  -0.018 0.985572    
## stat7       -2.630e-02  7.348e-02  -0.358 0.720393    
## stat8        4.434e-03  7.370e-02   0.060 0.952023    
## stat9       -4.175e-02  7.346e-02  -0.568 0.569815    
## stat10      -7.659e-02  7.371e-02  -1.039 0.298814    
## stat11      -9.635e-02  7.404e-02  -1.301 0.193245    
## stat12       4.989e-02  7.322e-02   0.681 0.495697    
## stat13      -1.318e-01  7.321e-02  -1.800 0.071850 .  
## stat14      -2.966e-01  7.314e-02  -4.055 5.08e-05 ***
## stat15      -4.843e-02  7.304e-02  -0.663 0.507365    
## stat16       4.115e-02  7.358e-02   0.559 0.575990    
## stat17      -1.786e-02  7.306e-02  -0.244 0.806862    
## stat18      -6.868e-02  7.303e-02  -0.940 0.347013    
## stat19       6.293e-02  7.319e-02   0.860 0.389899    
## stat20      -1.045e-01  7.295e-02  -1.433 0.151889    
## stat21      -8.647e-03  7.393e-02  -0.117 0.906883    
## stat22      -7.656e-02  7.329e-02  -1.045 0.296212    
## stat23       1.893e-01  7.310e-02   2.589 0.009650 ** 
## stat24      -1.025e-01  7.352e-02  -1.394 0.163472    
## stat25      -1.338e-01  7.333e-02  -1.825 0.068121 .  
## stat26      -7.923e-02  7.337e-02  -1.080 0.280274    
## stat27       1.025e-01  7.344e-02   1.396 0.162865    
## stat28       4.467e-02  7.384e-02   0.605 0.545246    
## stat29       7.528e-02  7.399e-02   1.017 0.308980    
## stat30       8.061e-02  7.409e-02   1.088 0.276634    
## stat31      -2.678e-02  7.421e-02  -0.361 0.718246    
## stat32       9.231e-04  7.410e-02   0.012 0.990061    
## stat33      -1.255e-01  7.315e-02  -1.715 0.086333 .  
## stat34       7.594e-02  7.359e-02   1.032 0.302142    
## stat35      -1.595e-01  7.358e-02  -2.168 0.030182 *  
## stat36       3.637e-02  7.284e-02   0.499 0.617565    
## stat37      -1.068e-01  7.397e-02  -1.444 0.148902    
## stat38       1.143e-01  7.377e-02   1.549 0.121483    
## stat39      -2.184e-02  7.304e-02  -0.299 0.764909    
## stat40      -3.365e-02  7.337e-02  -0.459 0.646567    
## stat41      -1.953e-01  7.326e-02  -2.666 0.007692 ** 
## stat42      -7.526e-02  7.361e-02  -1.022 0.306620    
## stat43      -7.189e-02  7.344e-02  -0.979 0.327684    
## stat44       3.912e-02  7.353e-02   0.532 0.594714    
## stat45      -1.307e-01  7.339e-02  -1.781 0.074906 .  
## stat46       7.951e-02  7.378e-02   1.078 0.281220    
## stat47       2.969e-02  7.422e-02   0.400 0.689140    
## stat48       4.386e-02  7.360e-02   0.596 0.551200    
## stat49       7.005e-02  7.311e-02   0.958 0.337997    
## stat50       4.491e-02  7.286e-02   0.616 0.537615    
## stat51       6.669e-02  7.354e-02   0.907 0.364553    
## stat52       1.284e-02  7.344e-02   0.175 0.861259    
## stat53      -1.829e-02  7.379e-02  -0.248 0.804209    
## stat54      -1.270e-01  7.415e-02  -1.712 0.086943 .  
## stat55       8.737e-02  7.319e-02   1.194 0.232611    
## stat56      -4.779e-02  7.367e-02  -0.649 0.516547    
## stat57      -1.705e-02  7.292e-02  -0.234 0.815096    
## stat58      -1.597e-03  7.266e-02  -0.022 0.982461    
## stat59       8.355e-02  7.350e-02   1.137 0.255673    
## stat60       1.489e-01  7.393e-02   2.015 0.044001 *  
## stat61      -9.188e-02  7.370e-02  -1.247 0.212567    
## stat62      -3.451e-02  7.350e-02  -0.470 0.638681    
## stat63       6.928e-02  7.363e-02   0.941 0.346787    
## stat64      -6.422e-02  7.332e-02  -0.876 0.381128    
## stat65      -5.599e-02  7.382e-02  -0.758 0.448269    
## stat66       1.197e-01  7.396e-02   1.618 0.105746    
## stat67      -5.111e-02  7.401e-02  -0.691 0.489861    
## stat68      -2.164e-02  7.364e-02  -0.294 0.768932    
## stat69      -4.749e-02  7.343e-02  -0.647 0.517838    
## stat70       2.141e-02  7.308e-02   0.293 0.769559    
## stat71      -8.116e-03  7.293e-02  -0.111 0.911394    
## stat72       6.709e-02  7.381e-02   0.909 0.363397    
## stat73       1.028e-01  7.365e-02   1.396 0.162737    
## stat74      -4.545e-02  7.365e-02  -0.617 0.537183    
## stat75      -9.223e-02  7.409e-02  -1.245 0.213227    
## stat76       5.184e-02  7.402e-02   0.700 0.483692    
## stat77      -6.491e-02  7.311e-02  -0.888 0.374659    
## stat78      -4.956e-03  7.355e-02  -0.067 0.946284    
## stat79      -8.268e-02  7.386e-02  -1.119 0.263011    
## stat80       1.379e-02  7.367e-02   0.187 0.851525    
## stat81       8.724e-02  7.369e-02   1.184 0.236512    
## stat82      -1.815e-02  7.318e-02  -0.248 0.804191    
## stat83      -4.484e-02  7.331e-02  -0.612 0.540797    
## stat84      -2.824e-02  7.373e-02  -0.383 0.701767    
## stat85      -3.663e-02  7.392e-02  -0.495 0.620278    
## stat86       1.554e-02  7.344e-02   0.212 0.832475    
## stat87      -1.215e-01  7.390e-02  -1.644 0.100184    
## stat88      -5.045e-02  7.294e-02  -0.692 0.489172    
## stat89      -1.183e-01  7.299e-02  -1.620 0.105281    
## stat90      -5.933e-02  7.359e-02  -0.806 0.420126    
## stat91      -1.171e-01  7.290e-02  -1.606 0.108296    
## stat92      -1.111e-01  7.354e-02  -1.510 0.131075    
## stat93      -8.753e-02  7.442e-02  -1.176 0.239594    
## stat94      -5.451e-02  7.364e-02  -0.740 0.459156    
## stat95      -3.554e-02  7.357e-02  -0.483 0.629081    
## stat96      -2.565e-02  7.326e-02  -0.350 0.726278    
## stat97       4.633e-02  7.308e-02   0.634 0.526134    
## stat98       1.055e+00  7.251e-02  14.552  < 2e-16 ***
## stat99       8.833e-02  7.414e-02   1.191 0.233550    
## stat100      1.771e-01  7.358e-02   2.408 0.016084 *  
## stat101     -3.040e-02  7.407e-02  -0.410 0.681523    
## stat102      4.741e-02  7.414e-02   0.640 0.522513    
## stat103     -9.285e-02  7.448e-02  -1.247 0.212553    
## stat104     -9.225e-02  7.373e-02  -1.251 0.210885    
## stat105      1.178e-01  7.285e-02   1.617 0.105887    
## stat106     -5.063e-02  7.329e-02  -0.691 0.489681    
## stat107     -6.439e-02  7.340e-02  -0.877 0.380402    
## stat108     -7.592e-02  7.324e-02  -1.037 0.299954    
## stat109      4.351e-02  7.300e-02   0.596 0.551214    
## stat110     -9.473e-01  7.322e-02 -12.937  < 2e-16 ***
## stat111     -7.480e-03  7.334e-02  -0.102 0.918767    
## stat112     -5.094e-02  7.345e-02  -0.693 0.488026    
## stat113     -5.709e-03  7.385e-02  -0.077 0.938380    
## stat114      2.782e-02  7.323e-02   0.380 0.703986    
## stat115      7.564e-02  7.310e-02   1.035 0.300867    
## stat116      8.560e-02  7.367e-02   1.162 0.245287    
## stat117      1.001e-02  7.361e-02   0.136 0.891820    
## stat118     -5.185e-02  7.285e-02  -0.712 0.476657    
## stat119      1.582e-02  7.381e-02   0.214 0.830343    
## stat120      4.674e-02  7.303e-02   0.640 0.522217    
## stat121     -2.791e-02  7.336e-02  -0.380 0.703595    
## stat122     -4.099e-02  7.315e-02  -0.560 0.575305    
## stat123     -3.522e-03  7.426e-02  -0.047 0.962176    
## stat124      3.054e-03  7.355e-02   0.042 0.966882    
## stat125     -5.011e-03  7.404e-02  -0.068 0.946037    
## stat126      1.059e-01  7.298e-02   1.451 0.146801    
## stat127      3.245e-02  7.325e-02   0.443 0.657795    
## stat128     -8.317e-02  7.320e-02  -1.136 0.255877    
## stat129      2.151e-03  7.325e-02   0.029 0.976577    
## stat130      8.812e-02  7.382e-02   1.194 0.232675    
## stat131      2.410e-02  7.352e-02   0.328 0.743062    
## stat132     -5.008e-02  7.285e-02  -0.687 0.491868    
## stat133      4.263e-03  7.312e-02   0.058 0.953503    
## stat134     -5.617e-02  7.308e-02  -0.769 0.442142    
## stat135     -8.440e-02  7.320e-02  -1.153 0.248946    
## stat136      6.623e-03  7.353e-02   0.090 0.928235    
## stat137      3.377e-02  7.293e-02   0.463 0.643365    
## stat138     -2.421e-02  7.343e-02  -0.330 0.741647    
## stat139     -8.458e-03  7.385e-02  -0.115 0.908816    
## stat140      2.616e-02  7.324e-02   0.357 0.721017    
## stat141      4.249e-02  7.292e-02   0.583 0.560108    
## stat142     -1.665e-02  7.431e-02  -0.224 0.822749    
## stat143     -2.452e-02  7.324e-02  -0.335 0.737845    
## stat144      8.083e-02  7.280e-02   1.110 0.266960    
## stat145      6.752e-02  7.467e-02   0.904 0.365945    
## stat146     -1.305e-01  7.389e-02  -1.766 0.077498 .  
## stat147     -7.574e-02  7.405e-02  -1.023 0.306386    
## stat148     -7.335e-02  7.268e-02  -1.009 0.312905    
## stat149     -1.197e-01  7.417e-02  -1.614 0.106565    
## stat150      2.415e-02  7.388e-02   0.327 0.743715    
## stat151     -1.248e-01  7.398e-02  -1.686 0.091756 .  
## stat152     -5.554e-02  7.321e-02  -0.759 0.448066    
## stat153      1.921e-02  7.430e-02   0.259 0.795936    
## stat154     -5.250e-02  7.421e-02  -0.707 0.479289    
## stat155     -3.410e-02  7.319e-02  -0.466 0.641328    
## stat156      1.341e-01  7.391e-02   1.814 0.069713 .  
## stat157      1.843e-02  7.306e-02   0.252 0.800881    
## stat158     -3.321e-02  7.407e-02  -0.448 0.653914    
## stat159     -3.405e-02  7.291e-02  -0.467 0.640469    
## stat160      1.743e-02  7.386e-02   0.236 0.813451    
## stat161      1.328e-01  7.402e-02   1.794 0.072890 .  
## stat162     -2.847e-02  7.293e-02  -0.390 0.696272    
## stat163      2.940e-02  7.441e-02   0.395 0.692755    
## stat164      4.814e-02  7.381e-02   0.652 0.514332    
## stat165     -5.498e-02  7.338e-02  -0.749 0.453671    
## stat166     -8.175e-02  7.289e-02  -1.122 0.262121    
## stat167     -9.384e-02  7.349e-02  -1.277 0.201657    
## stat168      2.385e-02  7.314e-02   0.326 0.744396    
## stat169      5.669e-02  7.350e-02   0.771 0.440560    
## stat170     -3.048e-02  7.363e-02  -0.414 0.678871    
## stat171      5.192e-02  7.401e-02   0.701 0.483020    
## stat172      5.933e-02  7.311e-02   0.811 0.417115    
## stat173     -4.352e-02  7.362e-02  -0.591 0.554391    
## stat174     -6.979e-05  7.314e-02  -0.001 0.999239    
## stat175     -9.160e-02  7.384e-02  -1.241 0.214828    
## stat176      1.977e-03  7.323e-02   0.027 0.978466    
## stat177     -1.259e-02  7.372e-02  -0.171 0.864349    
## stat178     -7.905e-02  7.436e-02  -1.063 0.287780    
## stat179      3.121e-02  7.346e-02   0.425 0.670936    
## stat180     -1.691e-02  7.306e-02  -0.231 0.817022    
## stat181      3.100e-02  7.381e-02   0.420 0.674497    
## stat182      4.466e-02  7.402e-02   0.603 0.546275    
## stat183      6.029e-02  7.343e-02   0.821 0.411587    
## stat184      2.268e-02  7.394e-02   0.307 0.759105    
## stat185      3.863e-03  7.277e-02   0.053 0.957664    
## stat186     -6.503e-02  7.421e-02  -0.876 0.380870    
## stat187     -1.030e-01  7.300e-02  -1.411 0.158219    
## stat188      2.154e-03  7.319e-02   0.029 0.976519    
## stat189      7.173e-02  7.353e-02   0.976 0.329346    
## stat190     -4.377e-02  7.317e-02  -0.598 0.549711    
## stat191     -5.542e-02  7.359e-02  -0.753 0.451408    
## stat192      2.072e-02  7.401e-02   0.280 0.779478    
## stat193     -7.708e-02  7.426e-02  -1.038 0.299337    
## stat194     -2.953e-02  7.362e-02  -0.401 0.688361    
## stat195      8.318e-02  7.352e-02   1.131 0.257894    
## stat196     -1.545e-02  7.436e-02  -0.208 0.835392    
## stat197     -4.307e-02  7.282e-02  -0.591 0.554285    
## stat198     -7.821e-02  7.334e-02  -1.066 0.286341    
## stat199      9.467e-02  7.271e-02   1.302 0.192987    
## stat200     -1.336e-01  7.278e-02  -1.836 0.066442 .  
## stat201      1.650e-02  7.350e-02   0.225 0.822320    
## stat202     -4.578e-02  7.392e-02  -0.619 0.535668    
## stat203      2.165e-02  7.346e-02   0.295 0.768184    
## stat204     -1.313e-01  7.310e-02  -1.796 0.072614 .  
## stat205     -8.076e-02  7.276e-02  -1.110 0.267069    
## stat206     -2.028e-02  7.407e-02  -0.274 0.784243    
## stat207      7.240e-02  7.354e-02   0.984 0.324912    
## stat208     -5.495e-03  7.318e-02  -0.075 0.940153    
## stat209     -8.333e-03  7.305e-02  -0.114 0.909178    
## stat210     -1.116e-02  7.375e-02  -0.151 0.879741    
## stat211      2.356e-02  7.313e-02   0.322 0.747348    
## stat212      4.807e-02  7.343e-02   0.655 0.512757    
## stat213     -7.699e-02  7.366e-02  -1.045 0.296003    
## stat214     -1.044e-01  7.319e-02  -1.427 0.153680    
## stat215     -7.739e-02  7.356e-02  -1.052 0.292792    
## stat216     -7.317e-02  7.350e-02  -0.996 0.319485    
## stat217      9.719e-02  7.365e-02   1.320 0.187053    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.659 on 5761 degrees of freedom
## Multiple R-squared:  0.2353, Adjusted R-squared:  0.2034 
## F-statistic: 7.385 on 240 and 5761 DF,  p-value: < 2.2e-16
cd.full = plot.diagnostics(model.full, data.train)

## [1] "Number of data points that have Cook's D > 4/n: 290"
## [1] "Number of data points that have Cook's D > 1: 0"

Checking with removal of high influence points

high.cd = names(cd.full[cd.full > 4/nrow(data.train)])
data.train2 = data.train[!(rownames(data.train)) %in% high.cd,]
model.full2 = lm(formula , data.train2)
summary(model.full2)
## 
## Call:
## lm(formula = formula, data = data.train2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.4782  -5.2219  -0.8294   4.7070  21.6283 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.162e+01  2.206e+00  41.530  < 2e-16 ***
## x1          -1.025e-01  1.534e-01  -0.668 0.504240    
## x2           7.402e-02  9.819e-02   0.754 0.450961    
## x3           1.573e-02  2.684e-02   0.586 0.557986    
## x4          -1.549e-02  2.120e-03  -7.307 3.12e-13 ***
## x5           2.218e-01  6.935e-02   3.197 0.001395 ** 
## x6          -1.629e-01  1.394e-01  -1.169 0.242557    
## x7           3.392e+00  1.493e-01  22.717  < 2e-16 ***
## x8           1.777e-01  3.489e-02   5.092 3.66e-07 ***
## x9           8.427e-01  7.780e-02  10.832  < 2e-16 ***
## x10          4.324e-01  7.282e-02   5.938 3.05e-09 ***
## x11          7.434e+07  1.739e+07   4.275 1.94e-05 ***
## x12          9.837e-03  4.404e-02   0.223 0.823254    
## x13          3.629e-02  1.762e-02   2.060 0.039479 *  
## x14         -7.668e-02  7.593e-02  -1.010 0.312559    
## x15          2.598e-02  7.205e-02   0.361 0.718413    
## x16          2.585e-01  4.999e-02   5.171 2.42e-07 ***
## x17          3.815e-01  7.621e-02   5.006 5.72e-07 ***
## x18          1.666e+00  5.320e-02  31.322  < 2e-16 ***
## x19          6.986e-02  3.878e-02   1.802 0.071663 .  
## x20          3.040e-02  2.698e-01   0.113 0.910308    
## x21          4.591e-02  9.926e-03   4.625 3.82e-06 ***
## x22         -2.106e-01  8.140e-02  -2.588 0.009689 ** 
## x23          3.180e-02  7.679e-02   0.414 0.678825    
## stat1        6.529e-03  5.826e-02   0.112 0.910770    
## stat2       -2.425e-02  5.829e-02  -0.416 0.677457    
## stat3        1.625e-01  5.820e-02   2.791 0.005266 ** 
## stat4       -9.132e-02  5.852e-02  -1.560 0.118720    
## stat5       -2.452e-02  5.828e-02  -0.421 0.674000    
## stat6       -4.029e-02  5.860e-02  -0.688 0.491790    
## stat7       -3.501e-02  5.809e-02  -0.603 0.546707    
## stat8       -3.963e-02  5.832e-02  -0.679 0.496853    
## stat9       -5.379e-02  5.825e-02  -0.923 0.355828    
## stat10      -7.833e-02  5.833e-02  -1.343 0.179334    
## stat11      -8.886e-02  5.867e-02  -1.515 0.129953    
## stat12       3.251e-02  5.791e-02   0.561 0.574614    
## stat13      -8.737e-02  5.801e-02  -1.506 0.132072    
## stat14      -3.152e-01  5.794e-02  -5.440 5.55e-08 ***
## stat15      -1.241e-01  5.792e-02  -2.142 0.032224 *  
## stat16       6.364e-03  5.829e-02   0.109 0.913062    
## stat17      -3.243e-02  5.793e-02  -0.560 0.575613    
## stat18      -3.039e-02  5.781e-02  -0.526 0.599176    
## stat19       5.090e-02  5.817e-02   0.875 0.381567    
## stat20      -6.549e-03  5.775e-02  -0.113 0.909714    
## stat21      -1.683e-02  5.855e-02  -0.287 0.773841    
## stat22      -2.276e-02  5.796e-02  -0.393 0.694579    
## stat23       1.847e-01  5.799e-02   3.184 0.001458 ** 
## stat24      -7.747e-02  5.834e-02  -1.328 0.184272    
## stat25      -6.796e-02  5.810e-02  -1.170 0.242164    
## stat26      -1.311e-01  5.829e-02  -2.249 0.024574 *  
## stat27       3.445e-02  5.837e-02   0.590 0.555134    
## stat28       7.691e-03  5.852e-02   0.131 0.895441    
## stat29       6.623e-02  5.858e-02   1.131 0.258293    
## stat30       3.840e-02  5.854e-02   0.656 0.511847    
## stat31       4.396e-02  5.881e-02   0.747 0.454846    
## stat32      -1.810e-02  5.876e-02  -0.308 0.758021    
## stat33      -1.225e-01  5.803e-02  -2.111 0.034805 *  
## stat34       1.011e-01  5.828e-02   1.734 0.082886 .  
## stat35      -1.671e-01  5.835e-02  -2.864 0.004200 ** 
## stat36       1.363e-02  5.788e-02   0.235 0.813890    
## stat37      -5.675e-02  5.870e-02  -0.967 0.333714    
## stat38       1.166e-01  5.839e-02   1.997 0.045889 *  
## stat39       4.470e-03  5.776e-02   0.077 0.938322    
## stat40      -5.271e-02  5.810e-02  -0.907 0.364322    
## stat41      -2.181e-01  5.799e-02  -3.761 0.000171 ***
## stat42      -5.497e-02  5.828e-02  -0.943 0.345624    
## stat43      -6.626e-02  5.825e-02  -1.137 0.255383    
## stat44       3.415e-02  5.836e-02   0.585 0.558427    
## stat45      -9.391e-02  5.817e-02  -1.614 0.106492    
## stat46       3.137e-02  5.854e-02   0.536 0.592024    
## stat47       4.425e-02  5.878e-02   0.753 0.451649    
## stat48       1.541e-02  5.815e-02   0.265 0.791078    
## stat49       2.988e-02  5.802e-02   0.515 0.606627    
## stat50       5.704e-02  5.778e-02   0.987 0.323605    
## stat51       5.488e-02  5.832e-02   0.941 0.346709    
## stat52       1.594e-02  5.835e-02   0.273 0.784707    
## stat53      -1.800e-02  5.837e-02  -0.308 0.757847    
## stat54      -1.353e-01  5.902e-02  -2.292 0.021949 *  
## stat55       5.806e-02  5.801e-02   1.001 0.316992    
## stat56      -3.793e-03  5.840e-02  -0.065 0.948221    
## stat57      -9.178e-03  5.796e-02  -0.158 0.874180    
## stat58      -2.139e-02  5.740e-02  -0.373 0.709404    
## stat59       1.216e-01  5.812e-02   2.093 0.036376 *  
## stat60       1.604e-01  5.866e-02   2.734 0.006269 ** 
## stat61      -1.126e-01  5.835e-02  -1.929 0.053752 .  
## stat62      -7.039e-02  5.813e-02  -1.211 0.225997    
## stat63       9.366e-02  5.844e-02   1.603 0.109031    
## stat64      -2.746e-02  5.804e-02  -0.473 0.636170    
## stat65      -2.773e-02  5.847e-02  -0.474 0.635270    
## stat66       7.156e-02  5.865e-02   1.220 0.222468    
## stat67       5.805e-02  5.862e-02   0.990 0.322109    
## stat68      -4.149e-02  5.831e-02  -0.711 0.476829    
## stat69      -6.837e-02  5.813e-02  -1.176 0.239608    
## stat70       6.633e-02  5.793e-02   1.145 0.252244    
## stat71      -1.868e-03  5.790e-02  -0.032 0.974269    
## stat72       6.323e-02  5.858e-02   1.079 0.280443    
## stat73       1.032e-01  5.847e-02   1.764 0.077717 .  
## stat74      -8.801e-03  5.830e-02  -0.151 0.880013    
## stat75      -3.407e-02  5.861e-02  -0.581 0.561020    
## stat76       3.009e-02  5.855e-02   0.514 0.607385    
## stat77       1.446e-02  5.791e-02   0.250 0.802792    
## stat78      -3.704e-02  5.804e-02  -0.638 0.523401    
## stat79       2.147e-02  5.838e-02   0.368 0.713145    
## stat80       1.695e-02  5.836e-02   0.290 0.771454    
## stat81       6.051e-02  5.842e-02   1.036 0.300347    
## stat82      -5.341e-02  5.794e-02  -0.922 0.356666    
## stat83      -3.738e-02  5.801e-02  -0.644 0.519332    
## stat84      -1.812e-02  5.847e-02  -0.310 0.756610    
## stat85      -1.130e-01  5.863e-02  -1.927 0.054046 .  
## stat86       5.573e-02  5.819e-02   0.958 0.338221    
## stat87      -1.039e-01  5.850e-02  -1.775 0.075928 .  
## stat88       2.253e-03  5.789e-02   0.039 0.968954    
## stat89      -5.473e-02  5.799e-02  -0.944 0.345342    
## stat90      -7.972e-02  5.834e-02  -1.367 0.171809    
## stat91      -1.115e-01  5.766e-02  -1.933 0.053226 .  
## stat92      -6.816e-02  5.824e-02  -1.170 0.241877    
## stat93      -3.492e-02  5.919e-02  -0.590 0.555266    
## stat94       2.246e-02  5.823e-02   0.386 0.699668    
## stat95       4.687e-02  5.835e-02   0.803 0.421858    
## stat96      -1.104e-02  5.813e-02  -0.190 0.849332    
## stat97       6.338e-02  5.787e-02   1.095 0.273439    
## stat98       9.648e-01  5.751e-02  16.777  < 2e-16 ***
## stat99       9.347e-02  5.883e-02   1.589 0.112180    
## stat100      1.500e-01  5.832e-02   2.572 0.010148 *  
## stat101      2.898e-02  5.875e-02   0.493 0.621792    
## stat102      5.430e-02  5.875e-02   0.924 0.355315    
## stat103     -9.929e-02  5.882e-02  -1.688 0.091493 .  
## stat104     -4.042e-02  5.856e-02  -0.690 0.490074    
## stat105      1.175e-01  5.768e-02   2.037 0.041714 *  
## stat106     -1.202e-01  5.799e-02  -2.074 0.038158 *  
## stat107     -3.054e-02  5.812e-02  -0.526 0.599252    
## stat108     -5.591e-02  5.809e-02  -0.962 0.335905    
## stat109      7.552e-04  5.788e-02   0.013 0.989591    
## stat110     -8.629e-01  5.789e-02 -14.905  < 2e-16 ***
## stat111      6.237e-03  5.799e-02   0.108 0.914352    
## stat112     -5.017e-02  5.823e-02  -0.862 0.388929    
## stat113     -9.494e-03  5.840e-02  -0.163 0.870860    
## stat114      6.319e-02  5.808e-02   1.088 0.276639    
## stat115      9.826e-02  5.798e-02   1.695 0.090166 .  
## stat116      6.625e-02  5.835e-02   1.135 0.256245    
## stat117      4.569e-03  5.815e-02   0.079 0.937381    
## stat118     -1.069e-02  5.763e-02  -0.185 0.852861    
## stat119      5.674e-02  5.846e-02   0.970 0.331861    
## stat120     -2.671e-02  5.776e-02  -0.462 0.643807    
## stat121     -1.087e-02  5.810e-02  -0.187 0.851536    
## stat122     -5.585e-02  5.808e-02  -0.962 0.336318    
## stat123      1.068e-01  5.882e-02   1.815 0.069582 .  
## stat124     -1.605e-02  5.826e-02  -0.275 0.783006    
## stat125     -1.385e-02  5.866e-02  -0.236 0.813388    
## stat126      6.977e-02  5.781e-02   1.207 0.227558    
## stat127     -3.234e-02  5.801e-02  -0.557 0.577293    
## stat128     -2.014e-01  5.792e-02  -3.477 0.000511 ***
## stat129     -1.094e-02  5.791e-02  -0.189 0.850233    
## stat130      7.619e-02  5.851e-02   1.302 0.192898    
## stat131      4.175e-03  5.825e-02   0.072 0.942869    
## stat132     -5.578e-02  5.768e-02  -0.967 0.333513    
## stat133      9.534e-03  5.801e-02   0.164 0.869463    
## stat134     -3.643e-02  5.782e-02  -0.630 0.528665    
## stat135     -7.629e-02  5.804e-02  -1.314 0.188742    
## stat136     -8.386e-03  5.823e-02  -0.144 0.885496    
## stat137      7.208e-02  5.778e-02   1.247 0.212280    
## stat138     -3.302e-02  5.812e-02  -0.568 0.569963    
## stat139     -1.631e-02  5.858e-02  -0.278 0.780756    
## stat140      1.552e-02  5.790e-02   0.268 0.788696    
## stat141      6.877e-02  5.780e-02   1.190 0.234161    
## stat142      8.816e-03  5.894e-02   0.150 0.881120    
## stat143     -5.405e-02  5.800e-02  -0.932 0.351422    
## stat144      8.790e-02  5.760e-02   1.526 0.127064    
## stat145      4.046e-02  5.927e-02   0.683 0.494868    
## stat146     -1.328e-01  5.861e-02  -2.265 0.023530 *  
## stat147     -7.392e-02  5.875e-02  -1.258 0.208366    
## stat148     -4.502e-02  5.775e-02  -0.780 0.435651    
## stat149     -1.166e-01  5.893e-02  -1.979 0.047830 *  
## stat150     -5.721e-03  5.871e-02  -0.097 0.922376    
## stat151     -2.396e-02  5.874e-02  -0.408 0.683390    
## stat152     -3.838e-02  5.794e-02  -0.662 0.507735    
## stat153      3.359e-02  5.880e-02   0.571 0.567890    
## stat154      3.264e-02  5.882e-02   0.555 0.579012    
## stat155      1.470e-02  5.808e-02   0.253 0.800169    
## stat156      1.076e-01  5.850e-02   1.839 0.065971 .  
## stat157      2.949e-04  5.780e-02   0.005 0.995930    
## stat158      4.493e-02  5.868e-02   0.766 0.443887    
## stat159     -1.003e-02  5.789e-02  -0.173 0.862388    
## stat160      3.010e-02  5.865e-02   0.513 0.607853    
## stat161      9.716e-02  5.861e-02   1.658 0.097462 .  
## stat162     -3.401e-02  5.773e-02  -0.589 0.555777    
## stat163      3.840e-02  5.916e-02   0.649 0.516264    
## stat164      1.273e-02  5.859e-02   0.217 0.827969    
## stat165     -2.131e-02  5.823e-02  -0.366 0.714423    
## stat166     -6.589e-02  5.764e-02  -1.143 0.253009    
## stat167     -1.473e-01  5.822e-02  -2.531 0.011405 *  
## stat168      5.791e-03  5.788e-02   0.100 0.920312    
## stat169      3.705e-02  5.843e-02   0.634 0.526046    
## stat170      1.251e-02  5.839e-02   0.214 0.830298    
## stat171     -3.397e-02  5.869e-02  -0.579 0.562735    
## stat172      1.083e-01  5.780e-02   1.873 0.061083 .  
## stat173      1.446e-02  5.826e-02   0.248 0.803961    
## stat174      5.670e-02  5.797e-02   0.978 0.328148    
## stat175     -9.406e-02  5.852e-02  -1.607 0.108077    
## stat176     -6.574e-02  5.799e-02  -1.134 0.256987    
## stat177     -9.208e-02  5.843e-02  -1.576 0.115103    
## stat178     -3.661e-02  5.889e-02  -0.622 0.534136    
## stat179     -9.267e-03  5.815e-02  -0.159 0.873399    
## stat180      5.192e-03  5.801e-02   0.090 0.928683    
## stat181      5.751e-02  5.846e-02   0.984 0.325257    
## stat182      8.915e-02  5.872e-02   1.518 0.129039    
## stat183      5.543e-02  5.838e-02   0.949 0.342492    
## stat184      1.112e-01  5.862e-02   1.897 0.057853 .  
## stat185      3.505e-02  5.770e-02   0.607 0.543571    
## stat186      4.305e-02  5.882e-02   0.732 0.464285    
## stat187     -2.516e-02  5.783e-02  -0.435 0.663469    
## stat188      2.084e-02  5.807e-02   0.359 0.719761    
## stat189      3.457e-03  5.828e-02   0.059 0.952699    
## stat190     -6.668e-02  5.794e-02  -1.151 0.249904    
## stat191     -3.506e-02  5.827e-02  -0.602 0.547420    
## stat192      1.508e-02  5.867e-02   0.257 0.797137    
## stat193     -9.102e-03  5.887e-02  -0.155 0.877134    
## stat194     -7.363e-02  5.843e-02  -1.260 0.207699    
## stat195     -9.135e-03  5.832e-02  -0.157 0.875533    
## stat196     -2.188e-02  5.902e-02  -0.371 0.710848    
## stat197     -6.382e-02  5.782e-02  -1.104 0.269707    
## stat198     -7.797e-02  5.806e-02  -1.343 0.179332    
## stat199      6.378e-02  5.765e-02   1.106 0.268598    
## stat200     -9.227e-02  5.782e-02  -1.596 0.110608    
## stat201      8.397e-02  5.839e-02   1.438 0.150477    
## stat202     -1.672e-03  5.869e-02  -0.028 0.977279    
## stat203      5.197e-02  5.809e-02   0.895 0.370967    
## stat204     -6.238e-02  5.799e-02  -1.076 0.282107    
## stat205      1.016e-02  5.743e-02   0.177 0.859590    
## stat206     -7.782e-02  5.872e-02  -1.325 0.185153    
## stat207      1.137e-01  5.838e-02   1.948 0.051438 .  
## stat208      2.788e-02  5.801e-02   0.481 0.630740    
## stat209     -1.444e-02  5.779e-02  -0.250 0.802756    
## stat210     -6.708e-02  5.844e-02  -1.148 0.251075    
## stat211      4.201e-02  5.802e-02   0.724 0.469058    
## stat212      8.656e-02  5.821e-02   1.487 0.137063    
## stat213     -2.598e-02  5.825e-02  -0.446 0.655636    
## stat214     -9.299e-02  5.803e-02  -1.602 0.109145    
## stat215     -5.089e-02  5.837e-02  -0.872 0.383374    
## stat216     -1.117e-01  5.812e-02  -1.921 0.054743 .  
## stat217      7.245e-02  5.838e-02   1.241 0.214692    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.458 on 5471 degrees of freedom
## Multiple R-squared:  0.3347, Adjusted R-squared:  0.3055 
## F-statistic: 11.47 on 240 and 5471 DF,  p-value: < 2.2e-16
cd.full2 = plot.diagnostics(model.full2, data.train2)

## [1] "Number of data points that have Cook's D > 4/n: 307"
## [1] "Number of data points that have Cook's D > 1: 0"
# much more normal residuals than before. 
# See if you can check the distribution (boxplots) of the high leverage points and the other points
model.null = lm(grand.mean.formula, data.train)
# summary(model.null)
# plot.diagnostics(model.null, data.train)
model.null2 = lm(grand.mean.formula, data.train2)
# summary(model.null2)
# plot.diagnostics(model.null2, data.train2)

Variable Selection

Basic: http://www.stat.columbia.edu/~martin/W2024/R10.pdf Cross Validation + Other Metrics: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/

Forward Selection (w/ full train)

Train

if (algo.forward == TRUE){
  t1 = Sys.time()
  
  model.forward = step(model.null, scope=list(lower=model.null, upper=model.full), direction="forward", trace = 0)
  print(summary(model.forward))
  #saveRDS(model.forward,file = "model_forward.rds")
  
  t2 = Sys.time()
  print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
  
  plot.diagnostics(model.forward, data.train)
}

Test

if (algo.forward == TRUE){
  test.model(model.forward, data.test, "Forward Selection")
}

Forward Selection (w/ filtered train)

Train

if (algo.forward == TRUE){
  t1 = Sys.time()
  
  model.forward2 = step(model.null2, scope=list(lower=model.null2, upper=model.full2), direction="forward", trace = 0)
  print(summary(model.forward2))
  #saveRDS(model.forward,file = "model_forward.rds")
  
  t2 = Sys.time()
  print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
  
  plot.diagnostics(model.forward2, data.train2)
}

Test

if (algo.forward == TRUE){
  test.model(model.forward2, data.test, "Forward Selection (2)")
}

Forward Selection with CV (w/ full train)

Train

if (algo.forward.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   , data = data.train
                                   , method = "leapForward"
                                   , feature.names = feature.names)
  model.forward = returned$model
  id = returned$id
}

Test

if (algo.forward.caret == TRUE){
    test.model(model.forward, data.test
             ,method = 'leapForward',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,id = id
             ,draw.limits = TRUE)
}

Forward Selection with CV (w/ filtered train)

Train

if (algo.forward.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train2
                                   ,method =  "leapForward"
                                   ,feature.names = feature.names)
  model.forward = returned$model
  id = returned$id
}

Test

if (algo.forward.caret == TRUE){
  test.model(model.forward, data.test
             ,method = 'leapForward',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,id = id
             ,draw.limits = TRUE)
}

Backward Elimination

Train

if (algo.backward == TRUE){
  # Takes too much time
  t1 = Sys.time()
  
  model.backward = step(model.full, data = data.train, direction="backward", trace = 0)
  print(summary(model.backward))
  #saveRDS(model.forward,file = "model_backward.rds")
  
  t2 = Sys.time()
  print (paste("Time taken for Backward Elimination: ",t2-t1, sep = ""))
  
  plot.diagnostics(model.backward, data.train)
}

Test

if (algo.backward == TRUE){
  test.model(model.backard, data.test, "Backward Elimination")
}

Backward Elimination with CV (w/ full train)

Train

if (algo.backward.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train
                                   ,method = "leapBackward"
                                   ,feature.names =  feature.names)
  model.backward = returned$model
  id = returned$id
}

Test

if (algo.backward.caret == TRUE){
  test.model(model.backward, data.test
             ,method = 'leapBackward',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,id = id
             ,draw.limits = TRUE)
}

Backward Elimination with CV (w/ filtered train)

Train

if (algo.backward.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train2
                                   ,method =  "leapBackward"
                                   ,feature.names = feature.names)
  model.backward = returned$model
  id = returned$id
}

Test

if (algo.backward.caret == TRUE){
  test.model(model.backward, data.test
             ,method = 'leapBackward',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,id = id
             ,draw.limits = TRUE)
}

Stepwise Selection (w/ full train)

Train

if (algo.stepwise == TRUE){
  t1 = Sys.time()
  
  model.stepwise = step(model.null, scope=list(upper=model.full), data = data.train, direction="both", trace = 0)
  print(summary(model.stepwise))
  #saveRDS(model.stepwise,file = "model_stepwise.rds")
  
  t2 = Sys.time()
  print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
  
  plot.diagnostics(model.stepwise, data.train)
}

Test

if (algo.stepwise == TRUE){
  test.model(model.stepwise, data.test, "Stepwise Selection")
}

Stepwise Selection (w/ filtered train)

Train

if (algo.stepwise == TRUE){
  t1 = Sys.time()
  
  model.stepwise2 = step(model.null2, scope=list(upper=model.full2), data = data.train2, direction="both", trace = 0)
  print(summary(model.stepwise2))
  #saveRDS(model.forward,file = "model_stepwise.rds")
  
  t2 = Sys.time()
  print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
  
  plot.diagnostics(model.stepwise2, data.train2)
}

Test

if (algo.stepwise == TRUE){
  test.model(model.stepwise2, data.test, "Stepwise Selection (2)")
}

Stepwise Selection with CV (w/ full train)

Train

if (algo.stepwise.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train
                                   ,method = "leapSeq"
                                   ,feature.names = feature.names)
  model.stepwise = returned$model
  id = returned$id
}

Test

if (algo.stepwise.caret == TRUE){
  # test.model(model.stepwise, data.test, "Stepwise Selection", draw.limits = TRUE, regsubset = TRUE, id = id, formula = formula)
  test.model(model.stepwise, data.test
             ,method = 'leapSeq',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,id = id
             ,draw.limits = TRUE)
}

Stepwise Selection with CV (w/ filtered train)

Train

Test

LASSO (w/ full train)

Train

if(algo.LASSO == TRUE){
  # Formatting data for GLM net
  # you can use model.matrix as well -- model.matrix creates a design (or model) matrix, 
  # e.g., by expanding factors to a set of dummy variables (depending on the contrasts) 
  # and expanding interactions similarly.
  x = as.matrix(data.train[,feature.names])
  y = data.train[,label.names]
  
  xtest = as.matrix(data.test[,feature.names]) 
  ytest = data.test[,label.names] 
  
  grid=10^seq(10,-2, length =100)
  
  set.seed(1)
  model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
  
  cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
  plot(cv.out)
  bestlambda<-cv.out$lambda.min  # Optimal penalty parameter.  You can make this call visually.
  
  print(coef(model.LASSO,s=bestlambda))
}

Test

if(algo.LASSO == TRUE){
  lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
  
  testMSE_LASSO = mean((ytest-lasso.pred)^2)
  print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
  
  plot(ytest,lasso.pred)
}

LASSO (w/ filtered train)

Train

if(algo.LASSO == TRUE){
  # Formatting data for GLM net
  # you can use model.matrix as well -- model.matrix creates a design (or model) matrix, 
  # e.g., by expanding factors to a set of dummy variables (depending on the contrasts) 
  # and expanding interactions similarly.
  x = as.matrix(data.train2[,feature.names])
  y = data.train2[,label.names]
  
  xtest = as.matrix(data.test[,feature.names]) 
  ytest = data.test[,label.names] 
  
  grid=10^seq(10,-2, length =100)
  
  set.seed(1)
  model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
  
  cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
  plot(cv.out)
  bestlambda<-cv.out$lambda.min  # Optimal penalty parameter.  You can make this call visually.
  
  print(coef(model.LASSO,s=bestlambda))
}

Test

if(algo.LASSO == TRUE){
  lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)  
  
  testMSE_LASSO = mean((ytest-lasso.pred)^2)
  print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
  
  plot(ytest,lasso.pred)
}

LASSO with CV (w/ full train)

Train

if (algo.LASSO.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train
                                   ,method = "glmnet"
                                   ,subopt = 'LASSO'
                                   ,feature.names = feature.names)
  model.LASSO.caret = returned$model
}

Test

if (algo.LASSO.caret == TRUE){
  test.model(model.LASSO.caret, data.test
             ,method = 'glmnet',subopt = "LASSO"
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,draw.limits = TRUE)
}

LASSO with CV (w/ filtered train)

Train

if (algo.LASSO.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train2
                                   ,method = "glmnet"
                                   ,subopt = 'LASSO'
                                   ,feature.names = feature.names)
  model.LASSO.caret = returned$model
}

Test

if (algo.LASSO.caret == TRUE){
  test.model(model.LASSO.caret, data.test
             ,method = 'glmnet',subopt = "LASSO"
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,draw.limits = TRUE)
}

LARS with CV (w/ full train)

Train

if (algo.LARS.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train
                                   ,method = "lars"
                                   ,subopt = 'NULL'
                                   ,feature.names = feature.names)
  model.LARS.caret = returned$model
}

Test

if (algo.LARS.caret == TRUE){
  test.model(model.LARS.caret, data.test
             ,method = 'lars',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,draw.limits = TRUE)
}

LARS with CV (w/ filtered train)

Train

if (algo.LARS.caret == TRUE){
  set.seed(1)
  returned = train.caret.glmselect(formula = formula
                                   ,data =  data.train2
                                   ,method = "lars"
                                   ,subopt = 'NULL'
                                   ,feature.names = feature.names)
  model.LARS.caret = returned$model
}

Test

if (algo.LARS.caret == TRUE){
  test.model(model.LARS.caret, data.test
             ,method = 'lars',subopt = NULL
             ,formula = formula, feature.names = feature.names, label.names = label.names
             ,draw.limits = TRUE)
}